Ecological Studies of Benthic Macroinvertebrates for Determining Sedimentation Impacts In

Home , Chimarra, Diplectrona, Eccoptura, Glossosoma, Parapsyche, Polycentropus

Ecological studies of benthic macroinvertebrates for determining sedimentation impacts in

Chattahoochee National Forest streams

Scott D. Longing

Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Entomology

J. Reese Voshell, Jr.

E. Fred Benfield

C. Andrew Dolloff

Richard D. Fell

Scott M. Salom

April 7th, 2006

Blacksburg, Virginia

Keywords: sedimentation, benthic macroinvertebrates, bioassessment, biological monitoring

Chattahoochee National Forest, Chattooga River watershed

Ecological studies of benthic macroinvertebrates for determining sedimentation impacts in

Chattahoochee National Forest streams

Scott D. Longing

ABSTRACT

Understanding sedimentation impacts to benthic macroinvertebrates in headwater, mountain streams is a top priority of watershed management programs in the Chattahoochee National Forest. Five studies involving the analysis of historical, biological survey data and current data were conducted to improve our understanding of macroinvertebrate response to sedimentation and to support the development of biological information for sediment load models to be applied in the Chattooga River watershed. An initial analysis of historical data involving a composited, macroinvertebrate reach-scale sample revealed weak relationships between assemblage metrics and sedimentation, which was similar to results of two recent macroinvertebrate studies that found biological ratings of good or excellent with reported physical impact attributed to sedimentation. Those findings and field reconnaissance in the Chattooga River watershed revealed that patchy, coarse sands may be the primary issue of concern regarding sedimentation impact to benthic macroinvertebrates. Therefore, a modified sampling approach was used to investigate relationships of macroinvertebrates and environmental conditions that included micro-habitat patches containing coarse sands, a product of erosion associated with Southern Blue Ridge, silicate parent geology.

At the microhabitat, patch scale, flow velocity was the main environmental factor associated with a macroinvertebrate assemblage gradient, and was significantly correlated with percent deposited sediment across 264 samples. The high dominance of just a few macroinvertebrate genera, and the majority lack of individual macroinvertebrate associations with dominant substrate types may suggest that the dominant macroinvertebrates utilize a multi-microhabitat portion of the streambed at any given time, which may be due to the homogenization of streambeds due to sand (providing ease of movement) and its immobility

(low bedload volume and sand patch shift). Because flow was the only significantly correlated environmental variable on an assemblage gradient produced by ordination (and was individually correlated with dominant substrate and percent deposited sediment), a subsequent study was conducted to determine macroinvertebrate sensitivity to deposited sediments among two flow-differentiated habitat types. Results showed that more taxa were related to a gradient of percent deposited sediment in fast water habitats, and no taxa were positively correlated with percent deposited sediment. Indicator species analysis found a number of taxa that were associated with a four-level grouping of percent deposited sediment levels.

Therefore, a final study involved calculating deposited sediment tolerance values using indicator species associations and individual cumulative abundances across percent deposited sediment levels. The final index developed from cumulative abundances showed a relationship with deposited sediment within the range 0 – 30%, and that low range was due to the low deposited sediment levels at which all 50% cumulative abundances fell (1 - 10%). The sedimentation index produced from indicator species analysis produced a reach-scale index that was related to percent pool embeddedness. Key findings from these studies are: (1) sand is the primary deposited sediment type, with most streambed comprised of cobble-sand substrate, (2) few taxa are associated with deposited sand substrate, (3) there are high numbers of a relatively few dominant taxa across samples and streams, (4) macroinvertebrate response to deposited sediments is greatest in fast water habitats, and (5) the developed sedimentation biotic index is a potential, assemblage-level indicator of increasing sedimentation in these headwater systems. The functional and habit organization of the four most dominant taxa determined in recent studies suggest that they may be utilizing sand patches for crawling and collecting food, therefore structurally adapting to long-term, press disturbances due to historical and contemporary anthropogenic activities and natural erosion. In addition, macroinvertebrate assemblage composition in these streams indicates overall good “health” and suggests streambed stability in the presence of a large portion of coarse sand. However, an important question that remains involves sand movement along streambeds and the ecological consequences of continued sediment inputs to these headwater systems.

In loving dedication to my parents, Ernie and Vicki Longing,

to Elly and Hannah,

and to the memory of C.D. (Pap) Longing.

iv Acknowledgements

A very special thanks to Dr. J. Reese Voshell, Jr. He gave me an opportunity of a lifetime in being a part of his aquatic entomology lab and I can only hope to one day practice the guidance and mentorship that he showed me. Most of all, through his thoughtful direction and friendship I persevered through graduate school during some tough years. My two very special lab-mates, Stephen Hiner and Dr.

Amy Braccia, always made me laugh and never were bothered to give a helping hand for anything.

Stephen inspired me to never get mad at anything and to always have a plan. When I arrived on campus,

Dr. Michael Moeykens was especially helpful in getting me situated.

I appreciate Dr. C. Dayton Steelman at the University of Arkansas for helping me get started and going on this graduate school road. I thank my Ph.D. graduate committee, Drs. E. Fred Benfield, C.

Andrew Dolloff, Richard D. Fell, and Scott M. Salom for their support and providing time and advice for a rookie scientist. A special thanks to Dr. Dolloff for providing research support through the USFS

Coldwater Fisheries Research Unit and for introducing me to northern Georgia. The support and help in the field of Craig Roghair, Jon Moran, and Dan Nuckols of the USFS Center for Aquatic Technology

Transfer were much appreciated. Charlene Breeden of the USFS Tallulah Ranger District in Georgia provided valuable advice and resources while working in the Chattahoochee National Forest. The effort of

Jamie Roberts is greatly appreciated as he came through in crunch-time by providing a helping hand during tough, north Georgia fieldwork. I am very grateful to several people who processed numerous macroinvertebrate samples: Trisha Voshell, Amanda Ellis, Rachel Wade, Kathy Hannah, and Hillery

Warner.

I greatly appreciate the kindness of the entire faculty and staff of the Virginia Tech Department of

Entomology, some who helped with scholastic endeavors and some who just listened and offered good advice, and friendship most of all. Some very special people helped me through some tough times and I’ll never forget their kindness. Dr. Ksenia Tcheslavskaia was a precious shoulder to lean on. I am also very grateful to Dr. Ashley Lamb for her kindness and for always offering a helping hand during some intense months of writing. A special thanks to Brian Eisenbeck for helping me outlast winter 2006 at the farm. I

v thank Dwight Paulette for allowing me to live on Kentland Farm; saying that living there made life more enjoyable and much easier is an understatement.

I thank two of the sweetest ladies in the world, Ann Germano and Elly Mildred Mead, for always supporting me and making family so special. I give dearest thanks and appreciation to my parents, Ernie and Vicki Longing, and my two sweet sisters, Amy and Sarah. Only through their never-ending love and support was I able to make it to where I am today. Finally, I acknowledge the two dearest little rascals in my life, Elly and Hannah. During the toughest times, I could always count on the love in my heart and thoughts of them to make me smile.

vi TABLE OF CONTENTS

ABSTRACT……………………………………………………………………………………………… ii

AKNOWLEDGEMENTS ………………………………………………………………………………. v

TABLE OF CONTENTS……………………………………………………………………………….. vii

LIST OF TABLES……………………………………………………………………………………… ix

LIST OF FIGURES…………………………………………………………………...... x

INTRODUCTION………………………………………………………………………………………. 1

Literature cited…………………………………………………………………………………. 8

CHAPTER 1: Relationships of sedimentation and benthic macroinvertebrate assemblages in Southern

Ridge streams as determined by systematic longitudinal sampling at the reach scale..………………… 14

Introduction……………………………………………………………………………………. 16

Methods………………………………………………………………………………………... 17

Results……………………………………………………………………………………...... 21

Discussion……………………………………………………………………………………... 23

Literature cited………………………………………………………………………………… 28

Tables…………………………………………………………………………………………. 31

Figures……………………………………………………………………………………….... 36

CHAPTER 2: Stream size, reach- and segment-scale environmental factors influencing benthic macroinvertebrates in Chattahoochee National Forest streams……………………………………….... 39

Introduction…………………………………………………………………………………… 41

Methods………………………………………………………………………………………. 42

Results………………………………………………………………………………………... 47

Discussion……………………………………………………………………………...... 50

Literature cited………………………………………………………………………………. 55

Tables………………………………………………………………………………………... 60

Figures……………………………………………………………………………………….. 63

CHAPTER 3: Benthic macroinvertebrate assemblages in forested, small streams in the Chattooga River watershed and exploratory analysis of influential environmental factors ………………..…………... 69

vii Introduction………………………………………………………………………………...... 71

Methods………………………………………………………………………………………. 72

Results………………………………………………………………………………………... 75

Discussion……………………………………………………………………………...... 79

Literature cited……………………………………………………………………………...... 84

Tables………………………………………………………………………………………… 87

Figures……………………………………………………………………………………….. 92

CHAPTER 4: Benthic macroinvertebrate relationships with deposited sediments in fast- and slow-water, mountain stream habitats………………………..…………………………………………………….. 110

Introduction………………………………………………………………………………….. 112

Methods……………………………………………………………………………………… 113

Results……………………………………………………………………………………….. 118

Discussion……………………………………………………………………………...... 120

Literature cited………………………………………………………………………………. 125

Tables………………………………………………………………………………………... 128

Figures……………………………………………………………………………………….. 132

CHAPTER 5: Development of a sedimentation biotic index for macroinvertebrates in small forested streams in the Chattahoochee National Forest…………………………………..…………………….. 136

Introduction………………………………………………………………………………….. 138

Methods……………………………………………………………………………………… 139

Results……………………………………………………………………………………….. 144

Discussion………………………………………………………………………………….... 145

Literature cited………………………………………………………………………………. 149

Tables………………………………………………………………………………………... 151

Figures……………………………………………………………………………………….. 154

SUMMARY OF CONCLUSIONS………………………………………………………………….... 157

VITA…………………………………………………………………………………………………. 161

viii LIST OF TABLES

Table 1-1 Streambed categorization scoring criteria (USEPA 1999)………………………………….. 31 Table 1-2 ANOVA of metrics among sedimentation categories………………………………………. 32 Table 1-3 Indicator species results for seven biological cluster groups………………...………….….. 33 Table 1-4 ANOVA of metrics among cluster groups…………………………………………..……… 34 Table 1-5 Spearman correlations of assemblage metrics and mean substrate variables…………….... 35

Table 2-1 PCA vector loadings for the first three axes using BVET variables……………...... …… 60 Table 2-2 Stepwise regressions metrics and streambed and channel habitat assessment variables…… 61 Table 2-3 Spearman correlations of segment scale BVET variables and mean reach scale substrate variables………………………………………………………………………………………………… 62

Table 3-1 Total macroinvertebrate abundances from 264 samples across eight streams……...... ….. 87 Table 3-2 Fifteen dominant taxa summary……………………………………………………………. 90 Table 3-3 Environmental variables and statistics for 264 samples across eight streams……..………. 91 Table 3-4 Spearman rank correlations of transect environmental variables (n = 264)…..…………… 91

Table 4-1 Benthic macroinvertebrate diversity among the two habitat types and broken down by stream……………………………………………………………………………………………….. 128 Table 4-2 Physical and biological characteristics of fast- and slow-water habitats. Mean (± 1 s.d. )... 129 Table 4-3 Spearman correlations of macroinvertebrates and deposited sediments …..………………. 130 Table 4-4 Spearman correlations of habitat and whole reach-specific metrics and sedimentation variables………………………………………………………………………………………………... 131

Table 5-1 Number of significant indicator species for seven sedimentation category categorizations. 151 Table 5-2 Indicator taxa of the most optimal sedimentation category designation...... ………………. 152 Table 5-3 Thirty dominant taxa tolerance values calculated from 50% cumulative abundances……. 153

ix LIST OF FIGURES

Figure 1-A Chattahoochee National Forest and Chattooga River watershed…………………….. ….. 12 Figure 1-B Headwater stream in the Chattahoochee National Forest…………………………….…… 13

Figure 1-1 Streams selected for macroinvertebrate sampling in 2001 and 2002……………………… 36 Figure 1-2 Cluster dendrogram of 30 reach-scale macroinvertebrate samples……………………….. 37 Figure 1-3 Indicator species analysis for cluster dendrogram group cutoffs………………………….. 38

Figure 2-1 Sampling locations in the Conasauga and Chattooga River watersheds..…………………. 63 Figure 2-2 DCA ordination and FFG and habit assemblage compositions……...... 64 Figure 2-3 Significant bivariate regressions of BVET variables and assemblage metrics…………….. 66

Figure 3-1 Eight study subwatersheds in the Chattooga River watersheds………………………….. 92 Figure 3-2 Systematic longitudinal transect sampling design and 100m channel gradients………….. 93 Figure 3-3 Frequencies of dominant substrate types among all samples……………………………... 94 Figure 3-4 Frequencies of sub-dominant substrate types among all samples………………………… 94 Figure 3-5 Frequency of percent deposited sediment among all samples……………………..……… 95 Figure 3-6 Overall relative abundance of the 15 dominant taxa per deposited sediment level…..….... 96 Figure 3-7 DCA ordination from a 264 sample x 37 taxa main matrix, with environmental overlay.... 99 Figure 3-8 DCA with Ephemera sp. represented by a symbol plot……….………………………….. 100 Figure 3-9 DCA with Amphinemura sp. represented by a symbol plot.…………………………….… 101 Figure 3-10 DCA with Tallaperla sp. represented by a symbol plot…..……………………………... 102 Figure 3-11 DCA with Epeorus sp. represented by a symbol plot……………………………………. 103 Figure 3-12 DCA with flow velocity overlain on main matrix………...... 104 Figure 3-13 DCA with flow velocity shown by a symbol plot...... ………… 105 Figure 3-14 DCA with deposited sediment shown by a symbol plot…………………………………. 106 Figure 3-15 DCA ordination of 64 sample x 64 species main matrix (12m replicates).……………… 107 Figure 3-16 DCA ordination of 12m replicates with outliers removed………………..……………... 108 Figure 3-17 Streambed profile showing substrate (from Platts et al. 1983)………………………….. 109

Figure 4-1 Eight study subwatersheds in the Chattooga River watersheds…………………………… 132 Figure 4-2 Mean (± s.d.) for (A) flow velocity and (B) water depth per habitat……………………… 133 Figure 4-3 NMS ordination of habitat samples per stream in species space………………………….. 134 Figure 4-4 NMS scree plot comparing the best run using real data with randomized runs…..………. 134 Figure 4-5 Dominant substrate occurrence among habitat samples combined across streams………. 135

x Figure 5-1 Eight study subwatersheds in the Chattooga River watersheds…………………………… 154 Figure 5-2 Regression of sample-scale SBI-CA (n = 127) and percent deposited sediment…………. 155 Figure 5-3 Regressions of SBI-IS (whole reach and habitat specific) and pool embeddedness……... 156

INTRODUCTION

In recent years, a large number of freshwater biomonitoring programs have been developed and implemented in direct response to legislation associated with the 1972 Clean Water Act (CWA). Although the overall objective of that legislation was: “to protect and restore the physical, chemical, and biological integrity of our nation’s waters”, execution of the CWA was more narrowly focused on point source pollution and a permit process aimed at municipalities and industries that contributed contaminants to streams and rivers. Amendments to the CWA in 1987 (section 319) included legislation mandating the assessment of physical, chemical, and biological conditions associated with non-point source pollution

(e.g., agricultural runoff, acid deposition, sedimentation). Although biological organisms were already used to help assess stream conditions before the CWA was amended, the development of biomonitoring procedures rapidly advanced because virtually all watersheds were considered to have been anthropogenically influenced by non-point source pollution. Accordingly, CWA section 305(b) established a process to conduct water quality assessments and required States, Tribes, and local governments to develop and implement effective bioassessment procedures and to report stream conditions biennially to the

U.S. Environmental Protection Agency. These circumstances led to the proliferation of studies associated with aquatic organism response to various non-point source pollutants as freshwater ecosystem stressors.

Benthic macroinvertebrates are essential components of stream ecosystems, and normal energy flow in those systems requires their involvement (Thorp and Covich 1991, Wallace and Webster 1996,

Covich et al. 1999). When associated with stream condition assessments, evaluating the response of benthic macroinvertebrates to pollution is advantageous in that they are directly and persistently affected by physical and chemical environmental conditions, as well as by influences from coexisting biological organisms. They are forced to deal with all types of environmental conditions because of their close association with streambeds and their sessile behavior, often not moving more than a few meters during the aquatic phase of their life cycle. Because benthic macroinvertebrates rely on streambeds for food and refuge, impacts to streambed conditions or water chemistry is manifested in macroinvertebrate assemblage traits (e.g., changes in organism abundances, taxonomic composition, densities, or functional attributes).

Because of the variety of macroinvertebrates typically present along streambeds that contribute to

heterogeneous communities, benthic macroinvertebrates can provide a wide range of information and assemblage characteristics that could potentially be affected by pollution.

Bioassessment information gained from sampling a fish or invertebrate population is typically summarized as assemblage metrics. Macroinvertebrate assemblage metrics are developed from taxa data and include measures of taxa richness, enumerations, functional and habit attributes and diversity and biological indices (Rosenberg and Resh 1995). Assemblage metrics are effective because they provide an assemblage summary, being ecologically descriptive and relatively easy to interpret by stream managers and the public. Karr (1981) originally developed and applied metrics for descriptions and comparisons of fish assemblages (Index of Biotic Integrity, IBI). Based on Karr’s metric concept and the application to fish assemblages, the use of metrics to describe benthic macroinvertebrate assemblages soon followed. The

USEPA published the first edition of the Rapid Bioassessment Protocols (Plafkin et al. 1989), which offered guidelines and suggestions for using fish and benthic macroinvertebrates as part of biological monitoring programs. In more recent times, studies have continued to focus on the development of effective benthic macroinvertebrate sampling strategies (Resh and McElravy 1993) and rapid assessment approaches to stream biomonitoring (Resh and Jackson 1993). Metrics used for describing aquatic assemblages, for comparing assemblages to biocriteria from reference conditions, and for ultimately determining waterbody biological conditions (within regional landscape constraints) continue to be an effective method for state freshwater biomonitoring programs (Stribling et al. 1998, Gerritson et al. 2000,

Pond and McMurray 2002).

It is well known that stream invertebrates are strongly influenced by the physical substrate conditions along streambeds (Cummins and Lauff 1969). Benthic macroinvertebrates have been shown to prefer specific streambed particle sizes (Cummins et al. 1964), and whole assemblages have been described according to general, physical habitat types (i.e., mud, gravel, bedrock, plant, Giller and Malmqvist 1998).

The array of particle sizes from silt to bedrock interact with stream hydrologic forces to form benthic habitats, which provide the template upon which overall stream function depends (Hynes 1970).

Sedimentation has been considered to be a major factor affecting streams and rivers across the

United States (Waters 1995). Persistent and unnatural levels of inorganic sediments can affect the particle size distributions along otherwise normal streambeds, thus altering habitats for benthic organisms. Fine

sediments can totally smother streambeds, which in turn can shift benthic macroinvertebrate assemblage structures to ones comprised mostly of sediment-tolerant individuals (Lenat 1994) and alter essential functional processes performed by benthic organisms at their intermediate trophic level (Wallace and

Webster 1996). Two classic reviews of the effects of inorganic sediments on aquatic biota are those of

Cordone and Kelley (1960) and Chutter (1969), the former dealing with the negative impacts of deposited sediments due to mining operations on invertebrates, fish, and aquatic plants, and the latter detailing sedimentation impacts to invertebrates in South American streams. More recently, Wood and Armitage

(1997) and Henley et al. (2000) have again suggested sedimentation as a major pollutant impacting the biology and overall function of stream systems. The American Society of Civil Engineers Task Committee on Sediment Transport and Aquatic Habitats comprehensively reviewed the associations of physical habitats, sedimentation, and aquatic biota in riverine environments and discussed how engineering approaches could be used to evaluate and predict those components (ASCE 1992).

Field studies, especially field experiments, dealing with sedimentation impacts are difficult because of the natural occurrence of inorganic fine sediments. The framework for biological assessment according to the latest edition of the USEPA Rapid Bioassessment Protocols (RBP III, Barbour et al. 1999) is to establish relationships of streambed habitats and aquatic biota and to ultimately infer water quality degradation when natural conditions (i.e., biocriteria) associated with those relationships change. When associated with sedimentation, difficulties exist in establishing impact because inorganic fine sediments occur as both macroinvertebrate habitat and a pollutant affecting macroinvertebrates in freshwater systems.

Some studies investigating fine sediment impacts to macroinvertebrate assemblages have relied on experimental additions of sediment (Rosenberg and Wiens 1978, McClelland and Brusven 1980) or from inputs due to streamside disturbances such as road construction (Lenat et al. 1981). Associated with biomonitoring, Angradi (1999) recently investigated macroinvertebrate assemblages in experimental trays associated with different percentages of deposited fine sediments and reported subtle treatment effects on macroinvertebrate metrics. In one of the few studies investigating natural levels of deposited sediments on macroinvertebrate assemblages, Zweig and Rabeni (2001) investigated metric response to a range of deposited sediment in Missouri Ozark Mountain streams and developed the tolerance-value based

Deposited Sediment Biotic Index (DSBI). In a review of sedimentation associated with U.S. streams and

rivers, Waters (1995) suggested that 30% cobble embeddedness was detrimental to benthic macroinvertebrates based on his review of several studies, while Kaller and Hartman (2004) recently reported a threshold level of fine sediment accumulation (particles < 0.25 mm exceeding 0.8 – 0.9% of riffle substrate composition) reduced Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa richness and diversity in small, West Virginia streams. Other than those studies, the effects of sedimentation on benthic macroinvertebrates, especially in headwater mountain streams, has been largely unexplored.

The Chattooga River (Georgia, North Carolina, and South Carolina, USA) is one of the southeast’s premiere whitewater rivers, of which the majority is owned (70%) by the United States Forest

Service (USFS). Most of the 170,000 acre Chattooga River Watershed is within the Chattahoochee-

Oconee National Forest located in northern Georgia, with smaller portions located in North and South

Carolina (Fig. 1-A). In 1974, 57 miles of the Chattooga River and the West Fork Chattooga River were designated as Wild, Scenic or Recreational by Congress as part of the 1968 Wild and Scenic Rivers Act.

This designation set forth guidelines for recreational use and protection from land development on a 15,000 acre corridor adjoining the river system. The Chattooga River watershed is viewed as both a significant recreational resource (e.g., hiking, boating, fishing, swimming, camping) and a forest-watershed resource

(e.g., timber management, wildlife habitat, fisheries). Recreational use and land development around the

Chattahoochee National Forest is increasing, partly attributable to the massive population (4 million) located south of the forest in Atlanta, Georgia. Therefore, increasing land-development and land-use pressure hastens the need to fully understand potential ecological impacts in the region.

The Chattooga River and many of its tributaries are showing signs of degradation due to sedimentation (USEPA 1999, Van Lear 1995). In 1999, the United States Environmental Protection

Agency (USEPA) reported on physical and biological conditions of streams within the CRW following settlement of the Georgia total maximum daily load (TMDL) lawsuit (Sierra 94-12501-1-CV-MHS). That comprehensive report listed overall stream conditions (i.e., very good to very poor) based on results of streambed habitat measurements associated with sedimentation and benthic macroinvertebrate sampling.

Streams falling below certain biological and physical criteria were listed as impaired, and some streams were placed on a "threatened" list due to potential negative impacts associated with sedimentation. Prior to the USEPA (1999) study, the Chattooga River Watershed Ecosystem Management Demonstration Project

was conducted from 1993 to 1995 to develop a watershed landscape geographic information system (GIS) dataset and to determine factors within the watershed that contributed to degraded physical and biological stream conditions. As part of that project, Van Lear et al. (1995) published a report on sources that contributed to sedimentation in the main stem of the Chattooga River, and Hansen (2001) thoroughly delineated stream networks in the Chattooga River watershed. English (1990) and Weber and Isely (1995) conducted surveys along the main stem of the Chattooga River and some of its major tributaries and rated stream condition based on benthic macroinvertebrate assemblages, but found that macroinvertebrate assemblages were generally in good condition at their study sites. Following the 1999 USEPA report on sedimentation and stream conditions in the CRW, stream monitoring programs were initiated by the USFS

Center for Aquatic Technology Transfer team (CATT, Roghair et al. 2002; Whalen et al. 2001). Through their efforts important datasets have been developed that will support long-term biological monitoring of headwater streams in the region. A current major goal in the Chattooga River watershed is to develop sediment load models by determining sediment budgets and the capacity for river networks to maintain normal characteristics without adverse environmental effects. An important component of that model involves understanding what impacts are being manifested in macroinvertebrate assemblages in streams subjected to sedimentation.

Determining biological responses to sedimentation in forested mountain streams is challenging because: (1) inorganic sediments naturally occur along streambeds, (2) the physical heterogeneity typical of mountain streams makes it difficult to establish consistent habitat units for assessing biological impact, (3) due to the high physical heterogeneity that provides habitat and refugia for macroinvertebrates, it could potentially take extreme amounts of deposited sediment to create changes in macroinvertebrate assemblages, as suggested by studies that have looked at sedimentation impacts due to severe streamside disturbances (e.g., road development). Additionally, the stressor - response (sedimentation - macroinvertebrates) relationship is vague because streams in the Chattooga River watershed are mostly forested at the present but have been heavily impacted by historical disturbances (Dolloff 1995, Harding et al. 1998). Therefore, investigating streambed conditions across unknown levels and vague histories of watershed disturbance creates problems in separating natural versus unnatural levels of sedimentation.

The overall goal of the following studies was to investigate benthic macroinvertebrates and their streambed habitats for determining overall sedimentation impact to the biota in Chattahoochee National

Forest headwater streams. Specific objectives of the studies contained in this dissertation were:

1. Analysis of existing biological survey data and physical habitat data to determine if that

information can reveal macroinvertebrate sensitivity across a range of reach-scale

sedimentation.

2. Determine environmental influences on macroinvertebrate assemblages associated with

stratified, multihabitat sampling.

3. Determine benthic macroinvertebrate assemblage characteristics and their relationships with

micro-habitat, patch scale environmental conditions including a range of deposited sediment.

4. Evaluate macroinvertebrate sensitivity to sedimentation among two primary habitat types in

headwater streams

5. Develop a sedimentation biotic index for monitoring sedimentation in the Chattooga River

watershed

For chapter 1 and chapter 2, previous survey data collected by the USFS Center for Aquatic

Technology Transfer was used to investigate the question of sedimentation impact to benthic macroinvertebrates and to improve our understanding of environmental influences on macroinvertebrates in streams throughout the Chattahoochee National Forest. The impetus for chapters 3, 4, and 5 was to determine the optimum scale at which sedimentation and macroinvertebrate relationships would be revealed. Therefore, we collected microhabitat scale samples randomly along systematic transects and recorded streambed environmental conditions that included various levels of deposited coarse sands.

Chapter 3 contained a taxonomic summary and exploratory multivariate analyses of environmental influences on benthic macroinvertebrates at a microhabitat, patch scale. In chapter 4, we determined macroinvertebrate sensitivity among two primary habitat types, and chapter 5 involved developing sedimentation tolerance values for macroinvertebrates. A final conclusion summarizes overall findings and

suggests strategies for biomonitoring and discusses implications regarding sedimentation impact to the aquatic biota in high gradient, headwater mountain streams.

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Fig 1-A. Map of southeastern U.S. showing the location of the Chattahoochee National Forest (shaded area, top) and the Chattooga River watershed (inset box). The wild and scenic corridor (shaded) is shown at bottom left. The mountain topography of the watershed is highlighted by the 10m digital elevation model (bottom right).

GA SC

Fig. 1-B. A typical mountain stream in the Chattahoochee National Forest.

CHAPTER 1

Relationships of sedimentation and benthic macroinvertebrate assemblages in Southern Blue Ridge

streams as determined by systematic longitudinal sampling at the reach scale

Abstract

In the Chattahoochee National Forest, stream management programs face the challenge of developing sediment load models for mountain streams impaired by sedimentation. Understanding the impact of sedimentation on the biological integrity of a waterbody will increase the effectiveness of those models and help conservation and restoration goals. In order to improve our understanding of sedimentation impacts to the biota in small, headwater streams in the region, we assigned thirty, 100m reaches to low, medium, and high sedimentation categories using existing methods for rating physical streambed conditions in the Chattooga River watershed. Assemblage metrics developed from a composited sample were tested among the sedimentation categories in order to determine overall macroinvertebrate sensitivity to sedimentation at the reach-scale. Assemblage metrics did not significantly vary among the sedimentation categories, which shows that physical sedimentation as indicated by low, medium and high sedimentation levels do not indicate altered macroinvertebrate assemblages. Our results are similar to other studies that reported physical sedimentation occurring in streams having good macroinvertebrate assemblage health. Using only biological data, we found reach groupings and interpreted those groupings using multivariate techniques and assemblage metrics. Seven biological groups revealed ecologically distinct patterns, with group assemblage differences associated mostly with functional feeding and habit traits. Additionally, cluster groupings of reaches within the same geographic location suggest inherent biological differences possibly due to stream type and watershed-scale characteristics. Our key finding shows that sedimentation, as determined by common substrate measurements, is not related to macroinvertebrate assemblages at the reach-scale and suggests the need for determining impact at appropriate scales through (1) refinement of streambed substrate measures, (2) sampling designs that acquire sensitive taxa.

Introduction

Sedimentation is a severe problem in most U.S. streams and rivers (Waters 1995) and has become a major concern in the Chattooga River watershed (Van Lear 1995, USEPA 1999). Much of the sedimentation problem here and throughout the Southern Appalachian region is partly attributable to erosion associated with historical timber harvests and valley clearing for agriculture that has lead to persistent, altered stream valley conditions (Dollof 1995, Harding et al. 1998). Widespread sedimentation within the Chattooga River watershed and other streams in the Chattahoochee-Oconee National Forest lead to a lawsuit in 1995 claiming that the Clean Water Act was not being enforced. The outcome of this legal action was a Consent Decree mandating that the Total Maximum Daily Load Program (TMDL) be implemented to assess the physical, chemical, and biological integrity of these streams and to restore the integrity of any streams that were determined to be impaired. In accordance with this mandate, biological assessments of benthic macroinvertebrates and physical characterizations of streambeds were conducted to determine the existing biological integrity and document the impacts of sedimentation on the observed biological conditions in these mountain streams (Roghair et al., 2002, Whalen et al., 2001).

Benthic macroinvertebrates are used more than any other organisms for biological monitoring in streams (Rosenberg and Resh 1993, Barbour et al. 1999). They are numerous in all types of freshwater habitats, and the many different taxa exhibit a wide range of ecological requirements for food, habitat, and water quality. In addition, they have a wide range of sensitivity to environmental pollution and other types of stressors, including sediment. Sensitivity to stressors is enhanced because benthic macroinvertebrates are typically sedentary and fairly long lived (1 year life cycles are most common). Most are not capable of short-term migrations away from adverse environmental conditions, and once benthic macroinvertebrate assemblages have been impacted by pollutants or other stressors they do not recover so quickly that the impairment goes undetected (Voshell et al. 1989).

Although benthic macroinvertebrates are effective for detecting impaired biological integrity, it is usually challenging to quantify cause and effect relationships with specific stressors, including sediment.

Part of the problem may be that benthic macroinvertebrate data inherently exhibit high variance because these organisms are microhabitat specialists. Small, adjacent locations with only slightly different physical

characteristics often contain different assemblages of organisms. This phenomenon is particularly obvious in mountain streams, which exhibit great physical streambed heterogeneity, thereby lessening the ability to determine discrete habitat-biota relationships. The effective quantification of sedimentation and the resulting degraded biological integrity as indicated by benthic macroinvertebrates has received only moderate attention, yet sedimentation is a key factor impacting aquatic systems. It is essential to quantify the effect of sediment on the benthic macroinvertebrate assemblage in order to assess the degree of impairment and establish a reliable baseline for tracking recovery when sedimentation TMDLs are implemented.

The overall purpose of this study was to determine what macroinvertebrate and sedimentation relationships could be documented by analyzing a pre-existing dataset that had been established by the U.

S. Forest Service (Roghair et al., 2002, Whalen et al., 2001) in response to the Chattooga River watershed

-TMDL Consent Decree. One specific objective was to evaluate a system for categorically ranking streams according to degree of impairment from sedimentation, which had been developed for this watershed by

USEPA (1999). Another objective was to elucidate distinct groups of sites based on benthic macroinvertebrate assemblages, then determine if the observed biological patterns were related to sedimentation. A third objective was to appraise what information about the relationships of benthic macroinvertebrates and sedimentation could be drawn from samples taken in different habitat types.

Methods

Study area

The Chattooga River watershed and the majority of the Chattahoochee-Oconee National Forest are located at the southern tip of the Blue Ridge physiographic region, with the Chattahoochee-Oconee

National Forest located entirely in northern Georgia. The Chattooga River watershed stretches across

North Carolina and South Carolina, but only the Georgia portion was included in this study. Mountain streams in the region are generally coldwater and high-gradient tributaries, ultimately draining into the

Savannah River. Watersheds are underlain by gneiss and shist rocks and loamy soils. Many of the watersheds are completely forested but have been historically disturbed by timber harvests, with some

streams and watersheds still showing scars of those activities in contemporary times (Dolloff 1995, Harding et al. 1998). Our sites were distributed across the Chattooga River watershed and at two additional, small regions in the Chattahoochee-Oconee National Forest. Those locations outside the Chattooga River watershed were (1) two stream reaches located south of the Chattooga River watershed in the Broad River subwatershed of the Savannah River, and (2) three reaches at the headwaters of the Chattahoochee River.

The remaining 25 sites were located in the Chattooga River watershed.

Field and Laboratory Methods

A total of 30 sites in 15 2nd and 3rd order streams (Strahler 1957) were visited for macroinvertebrate sampling and streambed susbtrate measurements in April and May of 2001 (19 sites) and

2002 (11 sites) (Fig 1-1). Eight streams had 3 or 4 sites on the same stream, and these sites were separated by ≈1km. The remaining sites were on separate streams. Sites were not placed near bridges or stream confluences. Figure 1-1 gives the location of three sampling regions within the Chattahoochee National

Forest, with GPS coordinates designating the point of sampling or the furthest downstream sampling site for those streams with multiple sites.

Macroinvertebrate sampling was conducted within a 100 m section at each site. To randomize the beginning point (i.e., downstream end) of each reach, a random distance (range 0-100 m) was stepped upstream, with the designated reach beginning at the end of that distance. Therefore, a 100m reach was located upstream and adjacent to the randomized beginning point and could have been located at any position within 200 m. One sample was taken from each reach by a multihabitat design that involved taking subsamples systematically along a longitudinal transect that ran parallel with the stream channel

(Lazorchak et al. 1998). Within each 100-m reach, macroinvertebrates were systematically sampled at 33 locations spaced 3 m apart on the longitudinal transect. At each sampling location, a measuring tape was extended across the stream channel perpendicular to the longitudinal transect and a random number was chosen to locate the sample a specified distance in 0.1-m increments from the left bank. A D-frame dip net

(500-µm mesh) was used to sample benthic macroinvertebrates at the specified locations. The net was held in one position on the stream bottom, and the streambed was agitated by hand directly upstream of the net to dislodge the organisms. The area sampled was visually estimated to be a square with sides equal to the width of the dip net (area ≈ 0.09m2). Because of the heterogeneous nature of the streambed and the

systematic and randomized sampling locations, a variety of substrate and current conditions were encountered. Therefore, the amount of time necessary to dislodge the organisms varied, but it generally ranged from 5 to 15 sec. The 33 subsamples were composited in a wash bucket (350-µm stainless steel mesh bottom) and preserved in 95% ethanol to yield one sample per reach. In the laboratory, a random

200-organism subsample was obtained from each sample by the gridded tray technique (Barbour et al.

1999). Each sample was washed onto a 25 x 50-cm tray that has a 350-µm stainless steel mesh bottom and is divided into 50 5 x 5-cm grids. Large leaves, sticks, and stones were first washed off and removed from the tray. Then, 5 x 5-cm grids were randomly chosen and all macroinvertebrates were sorted from the debris in those grids until a minimum of 200 macroinvertebrates were obtained. Grids were always sorted in their entirety once they had been selected. Organisms were identified to the lowest possible taxonomic level, which was usually genus, using standard taxonomic references applicable to the region (Brigham and

Brigham 1982, Merrit and Cummins 1996, Wiggins 1996). The numbers of organisms in each taxon were counted and the data were stored in Excel spreadsheets for later statistical analyses.

Streambed substrate measurements were made concurrently with macroinvertebrate sampling along the same 100-m reaches and included Wolman pebble counts (Wolman 1954) and percent embeddedness of cobble at pool tail-outs. Pebble counts (n = 100) were conducted in fast flowing areas typically comprised of cobble gravel substrate. The 100 pebble measurements were distributed over the fast flowing areas within the reach and generally involved pebble measurements at three to four locations along the reach. From the pebble count data, the variables D50 and percent fines less than 2mm were calculated (Table 1). Percent embeddedness was determined in pool tail-outs, which are the areas where the downstream section of the pool approaches the pool tail crest and was visually estimated to be the downstream, lower 25% of a pool. There, cobble substrate that does not reach the downstream riffle may accumulate and become embedded by transported fine sediments, thus providing a measure of deposited sediment for a stream reach. Percent embeddedness was calculated by dividing the embedded height of the cobble, indicated by a silt or sediment line, by the total height of the cobble along the axis perpendicular to that line.

Data analysis

One of the main reasons for this study was to see if benthic macroinvertebrate assemblages corresponded with the stream categorization system for impairment from sedimentation that had been developed for this watershed by the USEPA (1999). Thus, those same methods, as described below, were used to categorize the 30 sites as having either low, medium, or high sedimentation. A standardized scoring system was used so that the values for three substrate variables (D50 particle size, percent embeddedness, percent fines less than 2mm) could be combined (Table 1-1). Standardized values (0, 1, or

2) for each variable were summed and divided by 3 to yield the final sedimentation rank (range = 0.25 –

1.5). That range was trisected to establish low, medium, and high sedimentation categories.

From the benthic macroinvertebrate raw abundance data, metrics were developed to summarize assemblage characteristics for investigating biological patterns (Karr 1999). We chose our final metrics from a suite of thirty initial metrics considered to be good for detecting anthropogenic disturbance (Barbour et al. 1999), and we were specifically interested in those metrics that we thought should respond to increasing sedimentation. Metrics belonged to one of six categories: richness, community balance, community composition, functional feeding group, habitat, and tolerance. To reduce the number of test metrics and redundancy associated with similar metrics, Pearson correlation coefficients were computed between metrics within all five categories. If two metrics were significantly correlated (P < 0.05, r > 0.70), only one was retained. Our goal was to remove redundant metrics while retaining at least two metrics per ecological category. As a result, 17 metrics were retained for further statistical analyses.

ANOVA was used to determine if the 17 metrics varied significantly among the low, medium and high sedimentation categories. Metrics were tested for normality (Konglomorov-Smirnov tests) and transformed either by arcsine squareroot (proportions and percentages) or log (counts) prior to analysis. An alpha-level of 0.05 was used for all ANOVAs. In addition to determining metric relationships with sedimentation categories, relationships of metrics with the individual substrate variables were determined with Spearman correlations.

Following the evaluation of metric differences among the three sedimentation categories with

ANOVA, a series of multivariate analyses were conducted using just the biological data. A hierarchal, agglomerative cluster analysis (Ward’s linkage and Euclidean distance) on macroinvertebrate raw

abundances was used to explore other potential groupings of the 30 sites. To reduce subjectivity, and to most optimally describe cluster groups, an indicator species analysis was used to determine the number of groups that resulted in the largest number of indicator species and the lowest average P-value for all species

(McCune and Grace 2002). Indicator values were then determined for taxa associated with the derived biological groupings, where a statistically significant indicator value indicates that a taxon points to a particular group without error (i.e., IV = 100). P – values are calculated from Monte Carlo tests of significance using 1000 randomizations, where type 1 errors (α = .05) indicate the proportion of times an indicator value equals or exceeds the IV from actual data (McCune and Grace 2002).

To further describe cluster groups, ANOVA was used to determine if assemblage metrics varied among groups and indicator species analysis was used to show which taxa were associated with a particular group. In addition, relationships of the significantly different metrics among the cluster groups and individual substrate variables were determined by calculating Spearman correlation coefficients. ANOVAs and Spearman correlations were performed with Sigma Stat version 3.0. PC-ORD (MjM software,

Gleneden Beach, OR) was used for cluster analysis and indicator species analysis.

Results

Using the USEPA (1999) system for categorically ranking streams in this watershed according to degree of impairment from sedimentation resulted in 9 sites being ranked as having low sedimentation, 15 sites with medium sedimentation, and 6 sites with high sedimentation. ANOVA tests of 17 metrics among low, medium, and high sedimentation groups (Table 1-2) revealed only one significant metric, percent clingers (P = 0.003). Analysis of metrics with individual substrate variables showed that percent clingers was significantly correlated with D50 (r = 0.45, P = 0.012), % fines < 2mm (r = -0.49, P = 0.005), and % embeddedness (r = -0.40, P = 0.027). However, that was the only metric significantly correlated with individual substrate variables, and the correlations were rather weak.

The cluster dendrogram developed from log transformed macroinvertebrate abundances (Fig. 1-2) shows that sedimentation categories do not group together. Another interesting observation is that many of the streams contain reaches that were ranked differently according to the sedimentation categories,

suggesting high within-stream variation in physical and biological conditions. Three reaches were sampled in Addie Branch (AD), which is often used as a reference condition in the Chattooga River watershed and

Chattahoochee-Oconee National Forest due to minimal watershed disturbances. The cluster dendrogram showed that those three reference sites did not group together, which also indicates a moderate level of within-stream variability in assemblages. Thus, reference biocriteria would depend on the specific location from which it was collected within these reference streams. Some reaches showing biological similarity are located within the same small region of the Chattahoochee-Oconee National Forest. For example, the three sites in the Chattahoochee River headwaters (group 6; CT, JS, and LG) grouped together while three sites located south of the Chattooga River watershed in the Blue Ridge foothills and near the Piedmont transition zone (group 2; MB, NB, CB) had similar macroinvertebrate assemblages, yet were associated with all sedimentation categories (Fig. 1-2).

Seven stream groups were formed using cluster analysis on macroinvertebrate abundances (Fig. 1-

2). Stop-points for those seven cluster groups were aided by indicator species analysis that showed the highest number of indicator species occurred with seven groups, and the average P-value for all taxa was relatively low for the seven-group designation (Fig. 1-3). There were 24 significant indicator taxa distributed among the seven cluster-derived groups (Table 1-3) and a total of 11 significantly different metrics among those groups (Table 1-4). Group 1 had one indicator taxon, Chironomidae, with a significant but low indicator value. Besides the Tabanidae, Group 2 was associated with non-insect indicator taxa (Table 1-3). The streams in Group 2 that are located near the Blue Ridge/Piedmont transition zone are typically associated with lower channel gradients compared to the mountainous Blue Ridge region, therefore biological differences may be due to large scale physical variation. In Group 2,

Pleuroceridae showed perfect indication with an IV of 100. Group 4 had the single indicator taxon

Cordulegaster sp. Group 6 was comprised of three streams in the upper Chattahoochee River watershed and was strongly associated with Drunella sp. (IV = 100) and Cinygmula sp. (IV = 74.1). Eleven metrics were found to significantly vary among the seven cluster groups including Simpson’s diversity, percent 1 dominant, percent tolerant taxa, evenness, percent plecoptera, and percent Ephemeroptera. The remaining five metrics that were significantly different among our cluster groups were associated with functional feeding and habit metrics (Table 1-4).

Following the establishment of optimal cluster groups, we determined if the metrics that characterized our biological groupings were related to streambed sediment variables. Of the 11 significant metrics, only three were related to at least one substrate variable (Table 1-5). Our results show that distinct biological assemblages exist among our study streams, yet metrics used to characterize our distinct biological groupings were only weakly related to reach-scale streambed substrate measurements.

Discussion

In a recent study, Rabeni et al. (2005) investigated deposited sediment impacts to functional feeding group and habit group metrics and found a number of significant relationships, including density decreases in the number of clingers and sprawlers in the habit group. Similar to that finding, we found one assemblage metric that significantly varied across our sedimentation categories, percent clingers. That metric was also significantly related to all three individual substrate variables, where percent embeddedness and percent fines < 2mm were negatively associated and D50 was positively associated with percent clingers. That relationship is ecologically meaningful in regards to sedimentation, implying that non- embedded cobble provides a greater surface area for clinger organisms and increases in sedimentation will lessen the total habitat area, thus reducing the overall relative abundance of clingers. Therefore, percent clingers may prove to be an effective indicator of sedimentation in these headwater streams.

Streambed substrate variables often reported in habitat assessments and used to imply levels of sedimentation include substrate particle size distributions and percent embeddedness of a chosen substrate size (e.g., cobble embeddedness). However, such measures (i.e., pebble counts) are primarily intended for relative statistical comparisons of particle size distributions among test and reference sites (Bevenger and

King 1995, Bunte and Abt 2001), yet they are intuitively appealing for determining sedimentation levels because they are a direct substrate-size measurement (deposited sediments are typically substrate particle size < 2mm) and can indicate reach- or habitat-scale sedimentation. Measurements and visual estimations of habitat-specific streambed substrate are routinely used in habitat assessments that are conducted in conjunction with biological assessments (Environmental Monitoring and Assessment Program; Kaufmann

and Robinson 1998, North American Water Quality Assessment; Meador et al., 1993, Qualitative Habitat

Evaluation Index; Ohio EPA 1988, Rapid Bioassessment Habitat Assessment; Barbour et al., 1999) and are often used as indicators of deposited bedload sediments in TMDLs or other efforts aimed at reducing sediment loading to streams. Our findings suggest that at the reach scale in these headwater stream systems, those substrate measurements may be too coarse for detecting sedimentation impact at the scale of macroinvertebrates.

The USEPA (1999) reported that biological ratings based on a multimetric score were often in the

“good” range across a range of sedimentation-impacted streams. Weber and Isely (1995) calculated the

North Carolina Biotic Index (NCBI, Lenat 1993) for 52 sites in the Chattooga River drainage tributaries of the Chattooga River and reported an index value of “good” for five sites, with the remaining 45 sites receiving an “excellent” NCBI rating. The results of those studies suggest that common macroinvertebrate assemblage metrics and indices may not discriminate between physically impacted sites. Our study determined few relationships among macroinvertebrate assemblage metrics, sedimentation groups, and streambed substrate variables.

A problem with quantifying biological impacts from sedimentation may be that the methods and measurements used to quantify sedimentation are too coarse to effectively quantify stressor-response relationships. Most substrate measurement methods in streams were developed to assess reach-scale streambed conditions and to compare relative streambed measurements across numerous streams, such as particle size distributions. Most of the substrate measures used to assess physical habitat conditions are associated with fish habitat and are conducted at a spatial scale larger than that of benthic macroinvertebrate habitat. Thus, reach scale susbtrate measures may not relate to macroinvertebrate assemblages because there are adequate refugia and non-impacted habitats available. Furthermore, the potential impacts to macroinvertebrate assemblages due to sedimentation may be manifested within a spatial scale corresponding to the relatively small size and range of macroinvertebrates, with many differences in sensitivity among individual taxa. Such sensitivity may be undetected when using a composite, reach scale macroinvertebrate sample.

The substrate variables used to develop the low, medium, and high sedimentation groups were recorded throughout the 100-m reach (i.e., sample unit = 100 m). Therefore, only macroinvertebrate

samples that provided data corresponding to the substrate measurement locations may show potential relationships with a range of sedimentation. For example, embeddedness and pebble counts are pool-type and riffle-type substrate measures, respectively. Therefore, systematic transects nearest or adjacent to those habitats used for sedimentation variable measurements may provide sensitive organisms, yet compositing samples for the entire reach may weaken potential response signatures due to addition of samples not associated with those specific embeddedness and pebble count locations.

During our macroinvertebrate sampling, we probably encountered most benthic habitat types due to the random and systematic design. Relationships between our macroinvertebrate data and habitat data could be weak because of the wide range of available habitats, with sedimentation implied from only pool tail-outs and fast water riffles. The composite sample covering the 100-m reach may tend to mask relationships and dilute information for determining biota-habitat relationships. Although the systematic, longitudinal transect sampling design is advantageous in that sampling is both systematic and randomized, which can provide information on the relative proportion of habitats in a reach, the composite sample produced by the random, systematic sampling design may offer too much information and affect discrete stressor-response relationships from specific habitats. Additionally, compositing the sample may add an appreciable number of rare taxa that may come from unique habitat types. Further diluting the sample with those organisms from habitat types not associated with conditions from the substrate measurement locations may add noise to the already large range of assemblage data. Therefore, a systematic design that keeps transect samples separate and records streambed substrate conditions at those specific locations where macroinvertebrates are collected may reveal stronger habitat -biota relationships and provide more information about the effects of sedimentation. Aside from the fact that our composite sample may be too coarse for providing biological response signatures, the most important implication of our findings is that streams listed as threatened or impaired physically due to sedimentation may not be threatened or impaired biologically, a condition that seemingly could exist because of the persistent nature of streambed characteristics and general adaptive capabilities of freshwater macroinvertebrates.

Stream reaches that were located outside the Chattooga River watershed and proximal to one another grouped together in the cluster dendrogram (Fig. 1-1, Fig. 1-2). Those findings suggest that regional landscape characteristics may influence macroinvertebrate assemblages to a greater extent than in-

stream, physical streambed factors. The Chattahoochee-Oconee National Forest is comprised of eight ecological subsections that are classified according to vegetation, geology, and topography (Bailey 1987), and the development of reference criteria for monitoring sedimentation impacts should include natural variability due to those factors. Such physical variation due to large-scale watershed factors may correspond to varying threshold levels of sedimentation among regions, therefore requiring regionally- calibrated reference biocriteria. In potentially impaired sites, it is important to understand what portion of variance is due to regional landscape characteristics and what is due to anthropogenic watershed stressors, and that can only be ascertained through accurate reference biocriteria.

Although we determined significant differences in benthic macroinvertebrate assemblages among sites, there were only weak associations of the substrate conditions measured at the reach-scale with those biological measures. The lack of significant relationships between our biologically distinct cluster groups and streambed sedimentation suggests a need to modify physical streambed sampling designs. In order to accurately determine the effects of sedimentation at the scale of macroinvertebrates, methods need to be developed that accurately measure impacts to both fine-sediment sensitive macroinvertebrates and their habitats. Understanding those relationships will provide effective baseline information to serve biomonitoring program goals, including the effective integration of biological information into sediment- load models.

Acknowledgements

We thank Craig Roghair, Jon Moran, Dan Nuckols, and Keith Whalen of the USFS Center for Aquatic

Technology Transfer for acquiring field data and a very grateful to Stephen Hiner, Trisha Voshell, and

Rachel Wade for laboratory assistance. Thanks to Charlene Breeden of the USFS Tallulah Ranger District for providing resources in the Chattahoochee National Forest.

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habitat, macroinvertebrate community, stream sediment, and channel condition data collection

methodologies in the Chattooga River watershed, Chattahoochee National Forest, Georgia.

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Table 1-1 Description of streambed substrate variables and scoring criteria used by USEPA (1999) to combine variables into a stream categorization system for sedimentation impairment.

substrate variable description criteria and score D50 particle size particle size where the cumulative percent ≥ 32mm = 0

of half of the sample is finer than the given 16mm - 32mm = 1 size. ≤ 16mm = 2

percent fines < 2mm percent of substrate material in riffles that 0% – 25% = 0

is finer than 2mm. 26% - 50% = 1 ≥ 50% = 2

percent embeddedness measure of the amount of fine material ≤ 30 % = 0 surrounding cobble in the substrate. 30% - 50% = 1 ≥ 50 % = 2

Table 1-2 Results of ANOVA for metric comparison among low, medium and high sedimentation categories (USEPA 1999). ms = sum of squares between groups divided by degrees of freedom between groups. f = estimated population variation between groups divided by estimated population variance among groups.

statistics for between-category comparisons metrics df ms f P-value number of taxa 29 2.156 0.105 0.901 number of EPT* taxa 29 2.765 0.285 0.754

number of Ephemeroptera 29 0.003 0.322 0.215 Simpsons diversity** 29 0.001 0.188 0.902 Evenness*** 29 0.005 0.487 0.544 percent 1 dominant 29 0.000 0.004 0.996 percent Chironomidae 29 0.000 0.003 0.997 percent EPT 29 0.006 0.321 0.728 percent Plecoptera 29 0.000 0.364 0.838

percent Ephemeroptera 29 0.000 1.974 0.365 percent scrapers 29 0.002 0.989 0.385 percent shredders 29 0.005 1.174 0.324 percent clingers 29 0.044 7.205 0.003 percent burrowers 29 0.010 0.656 0.527

percent crawlers 29 0.004 0.456 0.638 percent tolerant 29 0.001 0.0825 0.921 number of intolerant taxa**** 29 0.010 1.149 0.563 *number of Ephemeroptera, Plecoptera, and Trichoptera **Simpsons diversity = 1 - sum (Pi*Pi) where Pi = importance probability in element i (element i relativized by row total)

***Evenness = H / ln (Richness), where H - sum (Pi*ln(Pi)) ****based on tolerance values in Lenat (1993). Values 6 - 10 as tolerant.

Table 1-3 Results of indicator species analysis for characterizing seven cluster groups. Indicator value indicates that a taxon points to a particular group without error (i.e., IV = 100). P – values are calculated from Monte Carlo tests of significance using 1000 randomizations, where type 1 errors (α = .05) indicate the proportion of times an indicator value equals or exceeds the IV from actual data.

stream group taxon indicator value (IV) P-value 1 Chironomidae 16.9 0.006

2 Oligochaeta 33.8 0.001 Pleuroceridae 100 0.002 Sphaeriidae 53.3 0.012 Tabanidae 55.6 0.023 Cambaridae 40.7 0.031

3 Paraleptoplebiidae 33.8 0.002 Diplectrona modesta 22.6 0.019

Atherix sp. 33.3 0.037 Tallaperla sp. 27.5 0.042

4 Cordulegaster sp. 31.4 0.028

5 Amphinemura sp. 23.4 0.012 Anchytarsus sp. 43.3 0.020 Acroneuria sp. 27.5 0.024

Yugus sp. 44.1 0.030 Epeorus sp. 27.8 0.043

6 Drunella sp. 100 0.003 Cinygmula sp. 74.1 0.004 Simulium sp. 39.3 0.019 Habropleboides sp. 48 0.023 Cheumatopsyche sp. 45.3 0.028

7 Leptophlebiidae sp. 50.3 0.007 Ameletus sp. 40 0.021 Parapsyche sp. 38.7 0.048

Table 1-4 Results of ANOVA for differences in macroinvertebrate assemblage metrics among seven derived cluster groups. Metrics significantly different among the seven cluster groups are in bold.

Formulas and description for Simpson’s diversity, evenness and tolerance are noted in table 1.2.

statistics for between-group comparisons metric df MS F p-value number of taxa 23 31.99 2.005 0.106

number of EPT taxa 23 0.005 1.086 0.400 number of Ephemeroptera 23 8.400 1.723 0.010 Simpsons diversity 23 0.022 3.783 0.014

Evenness 23 5.692 1.03 0.133 percent 1 dominant 23 0.035 4.989 0.002 percent Chironomidae 23 0.042 6.327 <0.001

percent EPT 23 9.847 1.091 0.397 percent Plecoptera 23 0.013 5.202 0.001 percent Ephemeroptera 23 0.024 3.183 0.020 percent scrapers 23 0.008 5.083 0.002 percent shredders 23 0.012 5.502 0.001 percent clingers 23 0.026 6.458 <0.001 percent burrowers 23 0.050 7.171 <0.001

percent crawlers 23 0.017 3.309 0.017 percent tolerant 23 0.052 5.01 0.002 number of intolerant taxa 23 0.003 1.166 0.405

Table 1-5 Spearman correlations of assemblage metrics and mean substrate variables among the seven stream groups determined by cluster analysis. Only correlation coefficients with P < 0.05 are shown. ns = not significant.

percent percent metric D50 % fines < 2mm % embeddedness Simpsons diversity ns ns ns percent 1 dominant ns ns ns percent Chironomidae ns ns ns percent Plecoptera ns ns ns percent Ephemeroptera ns -0.739 ns

percent scrapers ns ns ns percent shredders ns ns ns percent clingers 0.750 ns ns

percent burrowers -0.750 ns ns percent crawlers ns ns ns percent tolerant ns ns ns

number of intolerant taxa ns ns ns

Fig. 1-1 Streams selected for macroinvertebrate sampling in 2001 and 2002. Circles encompass streams located in different regions of the Chattahoochee National Forest and are designated in the table as ‘group.’

UTM coordinates, NAD 27 CONUS datum group stream east north 1 Bailey Branch* 293324 3871780 Addie Branch* 294061 3872126 Pounding Mill* 295294 3866278 Roach Mill* 287459 3863305 Law Ground* 299798 3868496 Reed Mill* 299331 3869252 Rock Creek* 285946 3863451 Finney Creek* 286066 3863031 Goldmine Branch 296155 3863710 Cutting Bone 285608 3855725 2 Upper Chattahoochee 246180 3847419 Jasus Creek 245549 3849420 Low Gap no signal no signal 3 North Fork Broad 281767 3827906 Middle Fork Broad 276327 3823793 *multiple sites per stream, coordinates indicate downstream point at which sampling began

Fig. 1-2 Results of cluster analysis of 30 benthic macroinvertebrate samples across 15 streams in the

Chattahoochee National Forest. Based on the cluster dendrogram and results of indicator species analysis stop-point procedures in figure 1-2, seven stream groups were selected (symbols and legend). The last letter of the site label indicates the sedimentation category (L, M, H). Seven stream groups based only on biological data were found by this cluster grouping and were supported by stop-point methods in figure 1-2

(information remaining at the seven-group cutoff is slightly less than 50% for group 4 and 5).

Distance (Objective Function) 1.9E+00 4.3E+01 8.4E+01 1.2E+02 1.7E+02

Information Remaining (%) 100 75 50 25 0 BA H RK L PM H group BA M 1 2 RH M 3 RD H 4 RK L 5 AD L 6 FN M 7 RH H RH M AD M FN M AD M RK M AD L FN M FN L RD M NB H CB L MB M RD H LW M PM M LW M GM M CT L JS L LG L

Fig 1-3 Results of indicator species analysis showing the number of significant indicators across different numbers of cluster groups (A), and the average P-value produced from Monte-Carlo randomizations (B).

These two graphs were used for stop-point procedures for the cluster dendrogram groupings in Figure 1.1.

Based on these graphs and the cluster dendrogram, the optimum number of groups shown was seven.

A. 30

indicators 10

5 number of significant significant of number

0 15141312111098765432 number of clusters

B. 0.350

0.300 -value

P 0.250

0.200 average average

0.150 15141312111098765432 number of clusters

CHAPTER 2

Stream size, reach- and segment-scale environmental factors influencing benthic macroinvertebrates

in Chattahoochee National Forest streams

Abstract

We investigated the relationships of macroinvertebrates and physical streambed conditions using an existing dataset developed from biological surveys conducted by the USFS Center for Aquatic

Technology Transfer to better understand the environmental factors shaping benthic macroinvertebrate assemblages in Chattahoochee National Forest headwater streams. Benthic macroinvertebrate assemblage characteristics developed from a stratified, composite multihabitat sample were determined along a stream size gradient, followed by an investigation of stream segment- and reach-scale environmental influences on macroinvertebrate assemblages. Assemblage metrics showed characteristic patterns due to differences in stream size, with a distinct functional feeding group and habit group shift. Fewer reach-scale environmental relationships were found at the larger sites when compared to those in smaller streams.

Percent embeddedness was the most frequent significant explanatory variable included in assemblage metric stepwise regression models. Pfankuch streambank rating and habitat assessment scores were significant predictors of macroinvertebrate assemblage metrics at the reach-scale. Significant explanatory variables at the segment-scale were all related to channel gradient and pool habitat structure, and biological variability over multi-kilometer stream distances (measured as average taxon and metric coefficients of variation) was significantly related to pool habitat structure. The key findings in our study are: (1) reach- scale organism-environment interactions are stronger in headwater reaches as compared to mid-order reaches, (2) substrate measurements at the reach scale are significant explanatory variables for assemblages acquired with a stratified, composite multihabitat sample, and (3) segment-scale channel structure significantly influences assemblages in mountain headwater streams. Our findings improve the understanding of abiotic influences on macroinvertebrate assemblages in these headwater systems and will aid the development of effective sampling designs used in stream bioassessments.

Introduction

The occurrence and distribution of benthic macroinvertebrates along streambeds are influenced by a variety of environmental factors. Environmental conditions from single-rock microhabitats to upland catchments directly affect assemblage organizations and the important stream trophic function those assemblages provide (Covich et al. 1999, Wallace and Webster 1996). At large spatial scales, benthic macroinvertebrates can show distinct assemblage patterns due to particular physiographic region (Brussock et al., 1985), zones created longitudinally along stream systems that are defined by energy supply and channel morphology (Vannote et al. 1980), or differences in geomorphic context within complex watershed landscapes (Montgomery 1999). Those large-scale features can influence water chemistry, erosion and fluvial processes that shape physical streambeds, and macroinvertebrate food resources at smaller scales.

Numerous studies have shown that benthic macroinvertebrates are affected by environmental factors operating over a wide range of spatial scales (Richards et al. 1997, Bis et al. 2000, Doisy and Rabeni 2001,

Nerbonne and Vondracek 2001, Sponseller et al. 2001, Cole et al. 2003, Death and Joy 2004).

Mountain streams are both unique in habitats and the types of macroinvertebrates present, and in the natural variability they possess (Finn and Poff 2005). Such streams are typically characterized as heterotrophic, with persistent organisms processing leaf inputs among physically variable streambeds and steep channel gradients (Beschta and Platts 1986, Downes et al. 1993). While headwater streams within a region are typically classified as one type, geomorphic features along a section of stream can create changes in channel form that may produce changes in overall biological characteristics (Montgomery

1999). Although distinct physical and biological characteristics shown by headwater streams facilitate an overall classification (e.g., Vannote et al. 1980), in-stream differences in physical features along physically heterogeneous stream channels can create spatially distinct biotic and abiotic conditions.

To develop effective biomonitoring programs associated with one or many pollutants, it is important to understand how the environment affects assemblage organization along streambeds. The establishment of relationships between biota and their environment strengthens bioassessment through effective sampling designs that compare macroinvertebrate populations that are similar in physical habitat

requirements. Thus, the mechanisms and characteristics associated with biological change due to the impacts of a particular stressor in those habitats are isolated, leading to precise comparisons.

When associated with streambed sedimentation, the problem of establishing links between biota and their environment is confounded because deposited sediments are a viable benthic habitat.

Furthermore, determining impacts to biota essentially requires understanding substrate influences on macroinvertebrates and the influence of the overall stream channel structure on those relationships. Those circumstances create difficulty in both quantifying streambed sedimentation and determining biological responses, with vague and often variable effects reported among studies. A recent study (Chapter 1) found weak relationships of macroinvertebrates and reach-scale susbtrate conditions in Chattahoochee National

Forest headwater streams. Those findings, produced from random and composited multihabitat samples, suggested high heterogeneity in both streambed substrate conditions and macroinvertebrate assemblages.

In this study, our main goal was to gain insight into the overall environmental mechanisms shaping assemblages in physically heterogeneous mountain streams and to apply that knowledge to the development of effective macroinvertebrate sampling designs. A stratified, multihabitat sampling design was used, with reach-scale substrate conditions and segment-scale habitat features measured concurrently, to determine if that sampling method would expose environmental factors influencing benthic macroinvertebrate assemblages. Specifically, we investigated macroinvertebrate relationships with abiotic factors associated with (1) reach-scale substrate conditions measured at several locations along stream channels, and (2) segment-scale channel structure and habitat conditions measured over entire stream segments and including reach-scale locations. Because the existing data also consisted of physical and biological measures among sites contrasting in size, we additionally investigated taxonomic shifts in assemblages due to the overall effect of stream size.

Methods

Study area

The Chattahoochee-Oconee National Forest is located at the southern tip of the Blue Ridge physiographic region in northern Georgia (Fig. 2-1). Mountain streams in the region are generally coldwater, high-gradient tributaries and catchment divides can reach elevations of 1500 m. Sites at two

regions of the forest were chosen for field investigations. The first region contained sites located in the

Conasauga River, which lies in the western portion of the Chattahoochee National Forest (Fig. 2-1). The source of the Conasauga River lies within the Cohutta Wilderness Area, and from there the river flows north-northwest across the Georgia state border into southeast Tennessee. The second region contained sites that were all located in the Chattooga River watershed (Fig. 2-1). Streams in the Chattooga River watershed are influenced by gneiss and shist rocks contained in loamy soils that are highly erodible (Van

Lear 1995) and are primarily underlain by sedimentary and metamorphic bedrock. The USFS has designated both the Conasauga and Chattooga River watersheds as two of the twelve large-scale watershed restoration projects in the nation (USDA Forest Service 2002).

The majority of public-land watersheds in the region are completely forested but have been historically disturbed by timber harvests, with some streams and watersheds still showing scars of those activities today (Dollof 1995, Harding et al. 1998). Because of that widespread historical disturbance and concern over sedimentation as a primary non-point source pollutant affecting freshwater biodiversity in the region, several recent studies have worked towards understanding the effects of sediments in aquatic systems, its appropriate measurement, and effective forest management in order to lessen its impact within aquatic systems (Riedel et al. 2002a, 2002b, 2003a, 2003b). When associated with bedload sediments, determining sedimentation impact to biota essentially requires understanding substratum influences on macroinvertebrates and the influence of the overall stream channel structure on those relationships.

Field and laboratory methods

Field sampling was conducted by the Center for Aquatic Technology Center of the U.S. Forest

Service. Data acquisition involved two spatial scales: (1) 100-m reach-scale, and (2) multiple-km segment- scale. Stream scale delineations were applied using the classification of Frissel et al. (1986). However, the approximate, longitudinal distances were adjusted because scale delineations were at intermediate distances between their classes. For example, the reach-scale was consistently 100 m (versus 101 m) and the segment scale varied from 0.9 to 3.8 km (versus 102 m, Frissel et al. 1986). Henceforth, reach-scale refers to (1) above, and segment-scale refers to (2). Segment scale involved one set of physical habitat variables per stream, while reach-scale variables were developed for all 100-m reaches and involved multiple reaches per stream separated by 1 km. Benthic macroinvertebrate sampling and habitat assessments began at each

point in Figure 2-1 and continued upstream at 1 km intervals, with the total distance sampled per stream ranging from 1.9 – 4 km.

Environmental variables at each 100-m reach location (n = 61) were developed from streambed substrate measurements, streambed habitat assessments using the Rapid Bioassessment Protocol III (RBP,

Barbour et a., 1999), and Pfankuch stream channel stability ratings (Pfankuch 1975). Streambed substrate variables were calculated from Wolman pebble counts (Wolman 1954), where 100 pebbles were measured along their intermediate axis using the heel-to-toe pebble selection method (Bevenger and King 1995).

From pebble counts, we calculated size-fraction variables including percent less than 2 mm in diameter, and D33, D50, and D84, which are the substrate diameter at which 33, 50, or 84% of the measured pebbles are below that specific substrate diameter, D (Bunte and Abt 2001). Percent embeddedness was determined in pool tail-outs, which are the areas where the downstream section of the pool approaches the pool tail crest and was visually estimated to be the downstream, lower 25% of a pool. There, cobble substrate that does not reach the downstream riffle may accumulate and become embedded by transported fine sediments, thus providing a measure of deposited sediment for a stream reach. Percent embeddedness was calculated by dividing the embedded height of the cobble, indicated by a silt or sediment line, by the total height of the cobble along the axis perpendicular to the sediment line. Rapid Bioassessment Protocol habitat assessments (RBP, Barbour et al. 1999) and Pfankuch channel stability ratings were developed from numeric scores relating to a series of habitat and channel condition observations along each 100 m reach, with the final value being the total sum of individual scores. However, those conditions scores (i.e., RBP and Pfankuch) are inverse of one another, where a large RBP value indicates good habitat conditions and a large Pfankuch value indicates poor streambank stability.

Segment-scale environmental variables were developed from stream channel habitat measurements using the intensive basinwide visual estimation technique (BVET) (Hankin and Reeves

1988, Dollof et al. 1993). BVET measurements were recorded beginning at the downstream 100-m reach and continued through the end of the last, upstream 100-m reach. The developed BVET variables reflect physical features of stream channel habitats and are typically used to assess fish habitat conditions (i.e., habitat surface areas, depths, and frequencies) and stream channel conditions including channel gradient, riparian widths, and numbers of large woody debris per size class (Table 2-1).

We used a stratified, multi-habitat sampling design to collect benthic macroinvertebrates from 61 reaches among 10 streams (Barbour et al. 1999). A D-frame net (500-µm mesh) was used to collect macroinvertebrates from five habitat types: (1) deep riffle, (2) shallow riffle, (3) pools, (4) leaf litter, and

(5) large woody debris (LWD). To reduce sampling bias, the substrate in each habitat was agitated for a standard time and flow velocity dictated appropriate number of net sweeps with each collection. Multi- habitat samples from within each 100-m reach were composited to yield one macroinvertebrate sample per reach. In the laboratory, a random 200-organism subsample was obtained from each sample by the gridded tray technique (Barbour et al. 1999). Each sample was washed onto a 25 x 50-cm tray that has a 350-µm stainless steel mesh bottom and is divided into 50 5 x 5-cm grids. Large leaves, sticks, and stones were first washed off and removed from the tray. Then, 5 x 5-cm grids were randomly chosen and all macroinvertebrates were sorted from the debris in those grids until a minimum of 200 macroinvertebrates were obtained. Grids were always sorted in their entirety once they had been selected. Organisms were identified to the lowest possible taxonomic level, which was usually genus, using standard taxonomic references applicable to the region (Brigham and Brigham 1982, Merrit and Cummins 1996, Wiggins

1996). The numbers of organisms in each taxon were counted and the data were stored in Excel spreadsheets until later statistical analyses.

Data analysis

Metrics were developed from the benthic macroinvertebrate raw abundance data to summarize assemblage characteristics for investigating biological patterns (Barbour et al. 1995, Karr 1999). We chose our final metrics from a suite of thirty initial metrics considered to be good for detecting anthropogenic disturbance (Barbour et al. 1999), and we were specifically interested in those metrics that we thought should respond to changes in substrate conditions due to increasing sedimentation. Metrics belonged to one of five categories: richness, community balance, community composition, functional feeding group, habitat, and tolerance. To reduce the number of test metrics and redundancy associated with similar metrics, Pearson correlation coefficients were computed between metrics within all five categories. If two metrics were significantly correlated (P < 0.05, r > 0.70), only one was retained. Our goal was to remove redundant metrics while retaining at least two metrics per ecological category. As a result, 17 metrics were retained for further statistical analyses. In addition, we developed two measures of biological variability to

investigate relationships with segment scale environmental conditions. Both measures were calculated as coefficients of variation (CV) over the entire segment scale and included (1) average CV of the 10 most dominant taxa, and (2) average metric CV.

A multivariate detrended correspondence analysis ordination (DCA) was used to initially explore assemblage patterns among all 61 sites. DCA provides a multivariate ordination in species space that seeks to extract pure community gradients (McCune and Grace 2002). To reduce the noise in our community data due to rare taxa, we used the delete-column command in PC-ORD to remove taxa that occurred in fewer than seven sites (≈ 10% of the total number of reaches). A multi-response permutation procedure

(MRPP) and indicator species analysis were conducted to further explore assemblage gradients associated with observed site groupings from the DCA ordination. MRPP is a non-parametric procedure for testing the hypothesis of no difference between two or more groups (McCune and Grace 2002). Indicator values

(IV) were determined for taxa associated with observed groupings among the site ordination, where a statistically significant indicator value indicates that a taxon points to a particular group without error based on frequency and abundance calculations (i.e., IV = 100). IV P – values are calculated from Monte Carlo tests of significance using 1000 randomizations, where type 1 errors (α = .05) indicate the proportion of times an IV equals or exceeds the IV from actual data. DCA, MRPP, and indicator species analysis were computed with PC-ORD software (MjM software design, Glenedon Beach, OR).

Stepwise multiple linear regression was used to investigate the influence of reach-scale environmental variables on the 17 macroinvertebrate assemblage metrics (forward selection, F = 4.0 to enter). Reach-scale environmental variables were scaled using a data centering relativization to reduce variable collinearity (Kleinbaum, Kuper, and Muller, 1988). Data centering was performed using the

‘adjust to mean’ relativization command in PC-ORD. Stepwise multiple linear regressions were calculated with Sigma Stat version 3.0 (SPSS Inc., Chicago, IL).

Relationships of segment-scale BVET variables and macroinvertebrate assemblage metrics were investigated with bivariate linear regression. The initial dataset containing BVET variables was reduced to a smaller set of variables using principal components analysis (PCA), with final selection of variables dependent upon their among-variable correlations within an axis (using PCA axes loadings) and the ability for the subset to describe measures thought to be most influential to benthic macroinvertebrates.

Additionally, because segment distances varied among our study streams, we discarded those variables dependent on the total segment distance sampled (e.g., percent total pool area). Finally, Spearman correlation coefficients were computed to show relationships among the segment scale BVET variables and mean reach-scale substrate variables per stream segment. Regression and correlation coefficients were computed with Sigma Stat version 3.0 (SPSS Inc., Chicago, IL).

Results

Site ordination

Detrended correspondence analysis ordination using all sites (n = 61) revealed one main assemblage gradient, with axes 1 and 2 explaining 78% of the variation in assemblages among macroinvertebrate samples. The most distinct assemblage pattern placed all Conasauga River sites at the extreme right end of axis 1 (Fig. 2-2A), with Chattooga River watershed sites distributed at the left end of axis 1. Those site groupings reflect the influence of stream size among our study sites (average width:

Conasauga River sites, 27.3 m; Chattooga River watershed sites, 4.8 m). Macroinvertebrates with the highest correlations with axis 1 included Leuctra sp., Diplectrona sp., and Tallaperla sp. associated with

Chattooga River watershed sites and Psephenus sp., Corydalus sp., and Stenelmis sp. associated with the

Conasauga River sites. Macroinvertebrate habit group and functional feeding group composition (Fig 2-

2B, 2-2C) supported the general concept that longitudinal differences in the composition of macroinvertebrate functional and habit groups are controlled by attributes associated with overall stream size (e.g., stream width, canopy cover, energy source, and dominant geomorphology, Vannote et al. 1980).

The relatively smaller streams in the Chattooga River watershed have a greater proportion of crawlers, and a lower proportion of clingers compared to the Conasauga sites (Fig. 2-2B). Clingers are typical of streams with open canopies that allow sunlight penetration and epiphyton growth on rocks, thus providing food for clinger organisms such as Psephenus sp., which had the highest positive correlation with Conasauga River sites along DCA axis 1 (Fig. 2-2A). An interesting note with the habit category is that the percentages of burrowers among the stream size gradient were equal (37%). Functional feeding group traits show a dominance of shredders (33%) associated with Chattooga River watershed sites, followed by collector-

gatherers (33%) and predators (16%). The functional feeding group organization in the Conasauga River includes collector-gatherers (39%), scrapers (34%) and collector-filterers (15%). The main contrast between the two groupings are the high proportion of shredders in the Chattooga River watershed sites and the high proportion of scrapers from the Conasauga River, a gradient indicative of a shift from heterotrophy to autotrophy (Rosi-Marshall and Wallace 2002).

Two other notable observations were made with the DCA ordination. First, a subset of river confluence reaches was observed within the Conasauga reach grouping at the positive end of axis 1 (Fig. 2-

2A). It has been shown that such confluences may disrupt the longitudinal connectivity of a system, therefore altering biological community structure at those locations (Rice et al. 2001, Poole 2002).

However, subsequent indicator species analysis based on macroinvertebrate frequencies and relative abundances between confluence sites and non-confluence sites showed that only one species, the stonefly

Pteronarcys sp, was significantly associated with the confluence reach grouping (IV = 50.0, Monte Carlo P

= 0.01). In the Chattooga River watershed, several road crossings were encountered while applying the 1 km-interval, systematic design along stream channels and at those locations we sampled at a 100-m reach immediately downstream of the crossing. The DCA ordination showed that road crossing sites were distributed throughout all other non-road sites at the negative end of axis 1, and a multi-response permutation procedure supported the observation that road crossing sites maintained no distinct macroinvertebrate assemblages (A = 0.001, P = 0.276).

Based on the results of the ordination, we investigated assemblage relationships with reach-scale environmental conditions among two separate data subsets (Chattooga River watershed sites and

Conasauga River sites) because any observed macroinvertebrate assemblage traits determined to be significantly related to environmental variables with all sites combined would be confounded by the influence of stream size. In separating those data subsets, analysis would reveal if environmental variables influenced assemblages differently across the contrasting stream size gradient.

Chattooga River watershed sites

Investigations of reach-scale environment influences on 17 macroinvertebrate assemblage metrics produced stepwise regression R2 values that were, although statistically significant, generally weak (R2 =

0.10 – 0.32, Table 2-2). Significant models were determined for a total of six assemblage metrics (Table 2-

2). Four of the six metrics included in significant models were functional feeding group or habit group metrics. Percent embeddedness was the most frequent and significant explanatory variable among metrics, included in number of Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT taxa), percent shredders, and percent crawlers models. However, of those three metrics, only number of EPT taxa were negatively associated with percent embeddedness. The variable percent fines < 2mm was a significant explanatory variable negatively related to percent scrapers. Also included in the percent scrapers model were the variables D33 and D84, with negative and positive associations, respectively. D84 was also a significant explanatory variable for percent clingers, suggesting that clinger organisms occurred within the reach that preferred relatively large substrate such as cobble, boulder or bedrock (i.e., large D84). The RBP habitat assessment measure was included in models explaining number of EPT taxa and number of intolerant taxa, with both showing positive relationships. Pfankuch streambank rating was a significant predictor of percent crawlers, percent shredders, and percent clingers.

Conasauga River sites

There were fewer significant relationships of reach scale measures from Conasauga River when compared to the Chattooga River watershed sites, suggesting weaker reach-scale environmental influence in larger systems (Table 2-2). However, three assemblage metrics were related to at least one significant, explanatory reach-scale measure. Percent fines < 2mm was included as an explanatory variables for percent shredders, yet was negatively associated with percent Plecoptera, suggesting that non-plecopteran shredders make up the largest proportion of the shredder population in the Conasauga River. Pfankuch streambank rating was a significant predictor of number of EPT taxa.

For both datasets, the variables percent fines < 2mm was not significantly correlated with percent embeddedness. Embeddedness is measured in pool-tail outs, while percent fines < 2mm is developed from pebble counts recorded from selected riffle habitats. The lack of relationships between those two variables, and their independent inclusion as explanatory variables in some metric stepwise regression models, suggests that sedimentation is affecting streambed habitats differently.

Influence of segment-scale BVET variables in the Chattooga River watershed

We reduced the segment-scale environmental dataset to 13 variables, with vector loadings for three axes shown in Table 2-1. In addition to using PCA, we chose variables that we considered were the

best descriptors of stream channel structure as it relates to benthic habitats (Table 2-1). Therefore, the 13 segment-scale variables derived from BVET calculations were each tested against the 17 macroinvertebrate assemblage metrics with bivariate regressions, for a total of 221 combinations. A total of 11 metric means were significantly related to at least one segment-scale environmental variable (Fig. 2-3). Channel gradient was the single most significant, explanatory variable related to percent EPT (R2 = .75, P = 0.024), percent

Plecoptera (R2 = .80, P = .006), number of Ephemeroptera (R2 = .77, P = 0.025), and percent shredders (R2

= 0.78, P = 0.018). Several assemblage metrics were related to pool structure including average taxon CV, number of EPT, and number of taxa (Fig. 2-3). Mean Simpson’s diversity index was negatively associated with mean residual pool depth and positively associated with percent pools with > 35% embeddedness.

The metric percent 1 dominant taxon was negatively related to percent pools with > 35% embeddedness.

Number of taxa and number of EPT taxa were both positively related to mean pool maximum depth.

Average taxon CV and average metric CV were also significantly related to pool habitat characteristics over our multi-kilometer stream segments. Average taxon CV was positively associated with mean pool area and average metric CV was negatively associated with percent pools as glides.

Correlations of reach- and segment scale environment

A total of 15 significant correlations were determined among all reach- and segment-scale environmental variables (Table 2-3). Some relationships were expected, such as significant, positive correlations of similar substrate measurements among segments (e.g., D33 vs. D50) or significant relationships of sediment conditions in pools (e.g., mean residual pool depth vs. percent embeddedness).

Mean RBP habitat assessment score and mean Pfankuch rating were both significantly correlated with percent pools with >35% embeddedness (Table 2-3). Pfankuch rating was negatively correlated with pebble count variables D33 (Spearman correlation coefficient (Sr) = -0.338, P = 0.023) and D50 (Sr = -

0.341, P = .022) and RBP habitat was positively correlated with D50 (Sr = 0.362, P = 0.014) and D84 (Sr

= 0.323, P = 0.030).

Discussion

A distinct macroinvertebrate assemblage gradient due to stream size was found with the DCA ordination, yet confounding factors may include the different physiographic provinces associated with the

reach groupings (i.e., all Conasauga River sites within the Ridge and Valley and the Chattooga River watershed sites located in the Blue Ridge geological province, Wallace et al., 1992). Landform differences among our two regions also reflect ecoregion subsection delineations of Bailey (1976), in that the

Conasauga in the metasedimentary subsection and the Chattooga River watershed in the Blue Ridge subsection. Additionally, many parts of the Conasauga River lie outside National Forest land and activities on private land (e.g., development and farming) may be influencing macroinvertebrate assemblage patterns.

However, the noted macroinvertebrate assemblage patterns (Fig 2-2A) strongly reflect biological patterns suggested by the River Continuum Concept (Vannote et al. 1980) in which longitudinal changes in assemblages are associated with stream size and distance from source (e.g., stream channel geomorphology and energy sources).

We did not observe any distinct biological changes due to road crossings. That finding suggests resilience of macroinvertebrate assemblages that are exposed to periodic disturbances at those sites.

However, impact typically increases with the rate of road use which is unknown for our study streams. The high heterogeneity in these streams may ameliorate impact due to road crossings and impact can be reduced through contemporary best management practices associated with road improvements and development.

We present two implications regarding our multiple habitat sampling design and our significant findings of metric-environment relationships. First, the design proved robust in developing macroinvertebrate assemblage traits responsive to environmental conditions at two scales. At the reach scale, environmental variables associated with discrete habitat types (i.e., riffle pebble counts and % embeddedness in pool tail outs), habitat assessment (i.e., RBP habitat assessment), and streambank conditions (i.e., Pfankuch) were all related to at least one substrate variable among Chattooga River watershed sites. Of the significant metrics, number of EPT taxa and percent scrapers showed negative relationships with variables indicative of streambed sedimentation (% embeddedness and % fines < 2mm, respectively). Because sedimentation has been found to be a major problem in the Chattooga River watershed (USEPA 1999), those measures may be effective for monitoring sedimentation impact if stratified multi-habitat sampling is used. In addition, RBP habitat was included as significant explanatory variables for number of EPT taxa and percent scrapers, which suggests its applicability for habitat assessments conducted during stream bioassessments.

An interesting result was that two metrics, percent shredders and percent crawlers, were positively related to % embeddedness. In the Chattooga River watershed streams, two shredder/crawler species

(Leuctra sp. and Tallaperla sp.) were among the three most dominant taxa in six of the eight streams.

Those taxa generally do not prefer pool habitat, but rather areas of moderate flow velocity, with leaf retention areas and clean substrate free of deposited sediments that would hinder leaf feeding activities.

However, percent shredders and percent crawlers were significantly explained by a negative relationship with the Pfankuch stream condition rating, meaning that as the Pfankuch rating increases and channels becomes less stable, those organisms increase in relative abundances.

The second implication is based on the fact that the multi-habitat sampling design may inherently reduce environmental variability, thus reducing the number of true assemblage relationships with environmental conditions. Whereas a random sampling design may indicate proportional areas covered by the entire range of streambed habitats, multi-habitat sampling seeks similar habitat units among sites.

Therefore, physical alterations to a particular habitat may lead to that habitat being discarded for sampling.

In that case, potential impacts due to physical habitat change may be missed, increasing type II statistical errors (i.e., failing to falsify the null hypothesis; failure to indicate impairment when it actually exists). The inability to detect differences among road sites and non-road sites, and the weak differences in Conasauga

River tributary-influences reached may be due to bias introduced by the stratified, multihabitat sampling design.

We were able to determine significant relationships of overall stream channel structure (i.e., segment- scale environment) with macroinvertebrate assemblage metrics. All significant relationships were related to either (1) channel gradient, or (2) pool habitat structure and conditions, yet channel gradient and variables related to pools were not significantly correlated (Table 2-3). Several metrics related to EPT taxa were significantly related to channel gradient, suggesting that channel gradient should be considered when designing field sampling procedures. Increases in mean pool maximum depth along the streams resulted in increases in the number of EPT taxa and the total number of taxa. Reach-scale environmental variable means significantly correlated with mean pool maximum depth were the Pfankuch streambank rating (Sr = -0.867, p = 0.025) and the RBP habitat score (Sr = 0.812, p = .050). That finding indicates that

as pool depth increases along a stream segment, streambed habitats and streambank stability generally increase towards optimum, good condition.

Biological variability measured over the longitudinal distances in streams showed ecologically- interpretable results. Stream average metric CV was negatively related to the percent pools as glides over the longitudinal stream distance. Glides are areas of uniform depth and substrate with flows intermediate of riffles and pools, and tend to homogenize a section of stream with those uniform flows and depths.

Therefore, increasing the percent area of glide habitat reduces habitat diversity, thus potentially reducing metric variability for those systems. Contrary to that finding, average taxon CV per stream was positively related to mean pool area, indicating that biological variability along a stream segment may increase as the percentage of pool habitats increase. Riffles are known to generally occur at intervals of 7x the stream width. Increasing pool area may shift that interval to a higher or lower occurrence, thus altering biological variability within a segment of stream.

A few reach-scale measures of streambed condition were found to be significant predictors of macroinvertebrate assemblage metrics. However, the high number of segment-scale relationships suggests that an overall habitat structure along stream segments is influencing macroinvertebrate assemblage patterns. Abrupt changes in confinement and channel bed slope may influence those patterns, with the ability to predict biota based on overall segment-scale habitat structure. Montgomery (1999) presented the process domain concept that suggests differences in local geomorphic context can create distinct regions within streams that may facilitate biological resilience or change due to disturbances. In our streams, those changes may be manifest in segment scale habitat characteristics that in turn influence the overall macroinvertebrate assemblage composition within headwater systems. By understanding relationships of these segment-scale physical characteristics and macroinvertebrate assemblages will increase the accuracy of bioassessments through avoiding biological comparisons among natural, physical variation.

Acknowledgements

We thank Craig Roghair, Keith Whalen, and Jon Moran of the Center for Aquatic Technology Transfer for collecting data, and Charlene Breeden of the USFS Tallulah Ranger district for providing resources in the

Chattahoochee National Forest. We are very grateful to Trisha Voshell, Stephen Hiner, and Rachel Wade for laboratory assistance.

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Table 2-1 Results of PCA for determining the BVET variables most associated with physical differences among six stream segments (1.9 – 4 km) located in Chattooga River headwater streams. PCA vector loadings for the first three axes are shown, with those axes accounting 72.5 percent of the physical variation among stream segments. Highlighted variables showing loadings >.20 were retained for later regression analyses with 17 assemblage metrics. BVET variable labels are given with descriptions and are used throughout the remainder of results.

BVET variable description axis 1 axis 2 axis 3 %Tpool area percent total pool area 0.20 -0.07 0.08 # pools number of pools 0.19 0.16 0.25 pools/km pools per kilometer -0.06 -0.13 0.27 T pool area total pool area 0.27 0.12 0.10 xParea mean pool area 0.23 0.09 -0.21 xPmaxD maximum pool depth 0.28 -0.08 0.08 xPaveD mean average pool depth 0.25 -0.14 0.01 xRPdepth mean residual pool depth 0.12 -0.23 0.12 %Triff area percent total riffle area -0.20 0.07 -0.08 # riffs number of riffles 0.16 0.21 0.23 riffs/km riffles per kilometer -0.10 0.07 0.24 Triff area total riffle area 0.24 0.16 0.03 xR area mean riffle area 0.07 0.05 -0.34 xRmaxD mean maximum riffle depth 0.18 0.04 -0.31 xRaveD mean average riffle depth 0.18 -0.09 -0.29 # LWD/km number of large wood debris per kilometer 0.17 -0.25 0.05 LWDRW number of large woody debris rootwad 0.08 0.15 -0.27 xrip width mean riparian width 0.15 0.09 -0.26 maxrip width maximum riparian width 0.20 0.16 -0.06 minrip width minimum riparian width 0.06 0.06 -0.25 %P_glide percent pools as glides -0.04 -0.23 -0.22 Pools >35% embed percent pools with > 35% embeddedness -0.12 0.31 -0.09 gradient channel gradient 0.24 0.12 0.17 cumulative percent variance explained by PCA axes 32.5 55.2 72.5

Table 2-2 Stepwise regression statistics of macroinvertebrate assemblage metrics and reach-scale

streambed substrate and stream channel habitat variables. Models include those metrics with significant (P

<0.05) predictor variables. Results are presented corresponding to separate data subsets from the

Conasauga River (CON) and Chattooga River watershed sites (CRW). For CON models n = 16, for CRW

models n = 45.

dataset model independent variable coefficient R2 F P-value CON number of EPT taxa Pfankuch -0.698 0.256 4.82 0.045

percent Plecoptera % fines < 2mm -0.581 0.253 4.74 0.047

percent shredders % fines < 2mm 0.546 0.278 5.39 0.036

CRW number of EPT taxa RBP habitat 0.645 0.180 9.31 0.004 % embeddedness -0.369 0.260 6.37 0.040

percent scrapers % fines < 2mm -0.670 0.105 16.8 <0.001 D33 -0.586 0.187 12.31 0.001 D84 0.349 0.322 8.18 0.007

percent shredders % embeddedness 0.554 0.101 10.01 0.003 Pfankuch -0.452 0.267 9.52 0.004

percent clingers D84 0.458 0.104 8.20 0.006 Pfankuch 0.303 0.200 5.07 0.030

percent crawlers % embeddedness 0.411 0.116 6.29 0.016 Pfankuch -0.426 0.231 9.64 0.003

number of intolerant taxa RBP habitat 0.621 0.181 9.52 0.004

Table 2-3 Spearman correlations (Sr) of segment scale BVET variables and mean reach scale substrate variables, metric CV, and taxon CV.

Relationships with P < 0.05 are highlighted in bold and underlined. BVET variable abbreviations are described in Table 2-1. Reach scale streambed

substrate variable means per stream segment and habitat assessment variables are indicated with the dashed line at top.

xP_area xPmax_d xRPdepth # riffs/k xR_area xRmax_d LWD/km xrip_w %P_glide %P_35 gradient embed %<2mm D50 D33 D84 Pfankuch RBP met CV tax CV # pools/k -0.15 0.48 0.30 0.38 -0.78 -0.33 0.66 0.00 -0.02 -0.45 0.28 -0.53 0.48 -0.72 -0.68 -0.73 -0.28 0.45 -0.06 0.08 xP_area 0.52 -0.34 0.00 0.67 0.94 0.47 0.81 0.29 -0.17 0.32 0.39 0.20 0.34 0.31 0.08 -0.22 0.20 -0.02 0.91 xPmax_d 0.45 0.18 0.06 0.32 0.73 0.28 -0.09 -0.76 0.67 -0.53 0.00 0.09 0.23 -0.07 -0.87 0.81 0.44 0.46 xRPdepth -0.41 -0.19 -0.35 0.36 -0.55 0.07 -0.82 0.01 -0.94 -0.19 0.01 0.13 0.08 -0.71 0.79 0.24 -0.48 # riffs/k -0.44 -0.23 -0.15 0.14 -0.78 0.39 0.73 0.15 -0.10 -0.16 -0.10 -0.16 0.02 -0.30 0.49 0.26 xR_area 0.81 -0.06 0.33 0.30 0.02 -0.02 0.44 -0.18 0.80 0.72 0.66 -0.05 -0.03 -0.01 0.44 xRmax_d 0.39 0.68 0.47 -0.09 0.11 0.49 0.29 0.46 0.33 0.23 -0.04 0.09 -0.23 0.85 LWD/km 0.32 0.52 -0.76 0.17 -0.39 0.63 -0.28 -0.33 -0.43 -0.46 0.73 -0.28 0.51 xrip_w 0.24 0.00 0.07 0.46 0.18 -0.15 -0.05 -0.39 -0.03 0.06 -0.10 0.73 %P_glide -0.32 -0.67 0.11 0.66 -0.17 -0.35 -0.25 0.21 0.22 -0.85 0.21 %P_35 -0.10 0.80 -0.07 0.10 -0.03 0.19 0.82 -0.99 -0.11 -0.01 gradient -0.14 -0.21 0.33 0.38 0.25 -0.50 0.18 0.70 0.44 embed 0.20 0.22 0.03 0.16 0.75 -0.81 -0.35 0.47 %<2mm -0.41 -0.71 -0.47 0.40 -0.03 -0.82 0.46 D50 0.91 0.96 -0.16 -0.10 0.36 0.20 D33 0.86 -0.42 0.09 0.64 0.06 D84 -0.07 -0.19 0.36 -0.03 Pfankuch -0.89 -0.64 -0.02 RBP 0.23 0.03 metric CV -0.14

Fig. 2-1 Southeastern U.S. showing the Conasauga and Chattooga River watershed. Below, Conasauga

River (left) and Chattooga River subwatersheds (right). For the Conasauga River, points represent individual sections where from 3 – 5 macroinvertebrate, multihabitat samples were collected. Points in the

Chattooga River subwatersheds represent the downstream sampling point, where samples were collected at

1km intervals going upstream. BVET variables were recorded beginning at those points and continued upstream along variables distances per stream (1.9 – 4 km).

West Fork subwatershed

Warwoman Creek subwatershed

Conasauga River

0361.5 Kilometers 0361.5 Kilometers

Fig. 2-2 Results of DCA with species overlain on a 65 site ordination (A), and habit and functional feeding group characteristic (B, C) from stream sites in the Chattooga River watershed and the Conasauga River.

The large environmental gradient due to stream size is shown by all Conasauga sites located at the right and all Chattooga River watershed sites located at the left side of the ordination graph, with taxa attributed to that environmental gradient overlain on the ordination. Axis 1 percent variance explained = 62%, axis 2 =

16%. Assemblage composition among the stream size gradient is detailed with (B) habit group, and (C) functional feeding group. CR = crawler, BU = Burrower, CLG = clinger, CL = climber, SK = skater, SP = specialist, GN = generalist, PR = predator, SH = shredder, CG = collector-gatherer, SC = scraper, CF = collector-filterer. “Con” prefix on ordination site labels = confluence site.

ROAD FS Axis 2

FS FS ROAD

ROAD

FS Phylocen Sphaerii confluen

Heteropl conJACKS Corduleg FS FS horse ca ROAD FS FS ROAD ROAD N. serr. confluen conJUP Psephenu ROAD Micrasem FS FS Dixa Boyeria FS Promores Tricoryt FS FS ROAD Dubiraph Hydropsy Rhyacoph Euryloph FS FS Diplectr Macronyc Stenelmi Axis 1 Leuctra FSFSFS ROAD FS ROAD FS confluen private Simulium Chimarra Cheumato Corydalu Tallaper Ceratopo FS FS Leucrocu Isonychi Pleuroce FS FS Tipula Serratel FS Baetis ROAD FS Dicranot FS FS FS FS ROAD ROAD Collembo ROAD FS ROAD FS ROAD FS FS ROAD FS ROAD ROAD

FS Eriopter FS

ROAD

Fig. 2-2 continued.

Chattooga River watershed sites Conasauga River sites

B. habit group %SP %CL %SK %GN %SP 1% 1% 0% %GN 0% 1% %CL %CR 1% 0% 11% %SK

%CLG 0% 18%

%CR 42% %CLG %BU 51% 37%

%BU 37%

C. functional feeding group %PR

%PR 10% 16% %SH %CG 2% 33% %CG 39%

%SC %SH 34% 33% %CF 9% %SC %CF 9% 15%

Fig. 2-3 Significant bivariate regressions of macroinvertebrate assemblage metrics and segment-scale

BVET variables among six Chattooga River headwater streams. P – values are calculated for t, which is the regression coefficient divided by its standard error. BVET variable abbreviations and descriptions are given in Table 2-1.

0.48 4.4 2 2 0.46 R = .80 R = .75 P = 0.006 4.2 P = 0.024 0.44

0.42 4.0

0.40 3.8 0.38 #EPT 0.36

%Plecoptera %Plecoptera 3.6

0.34 3.4 0.32

0.30 3.2 2.42.62.83.03.23.43.63.84.04.2 2.42.62.83.03.23.43.63.84.04.2 gradient gradient

0.48 2.2 2 2 0.46 R = .78 R = .77 P = 0.018 2.1 P = 0.025 0.44 2.0 0.42 0.40 1.9

0.38

%SH 1.8 0.36

# Ephemeroptera 1.7 0.34

0.32 1.6

0.30 1.5 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 2.42.62.83.03.23.43.63.84.04.2

gradient gradient

Fig. 2-3 continued

1.2 0.50 2 R = .80 R2 = .71 1.0 P = 0.015 0.45 P = 0.033 0.8 0.40

0.6 0.35

Taxon CV Taxon Metric CV 0.4 0.30

0.2 0.25

0.0 0.20 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 0.00.10.20.30.40.5 xP_area %P_glide

0.80 5.4 R2 = .70 R2 = .91 5.3 0.78 P = 0.035 P = 0.003

5.2 0.76

5.1 0.74

5.0 #TAXA 0.72

Simpsons Diversity 4.9

0.70 4.8

0.68 4.7 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.05.56.06.57.07.58.0 xRP depth xpmaxdept

Fig. 2-3 continued

0.80 0.80 R2 = .79 R2 = .70

0.78 P = 0.017 0.78 P = 0.035

0.76 0.76

0.74 0.74

0.72 0.72 Simpsons Diversity Simpsons Simpsons Diversity

0.70 0.70

0.68 0.68 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Pools >35% embed xRP depth

0.46 4.4 2 0.44 R = .68 4.2 P = 0.041 0.42

0.40 4.0

0.38 3.8 0.36 #EPT %1DOM 0.34 3.6 0.32 R2 = .73 3.4 0.30 P = 0.029

0.28 3.2 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 5.0 5.5 6.0 6.5 7.0 7.5 8.0 Pools >35% embed xPmax d

CHAPTER 3

Benthic macroinvertebrate assemblages in forested, small streams in the Chattooga River watershed

and exploratory analysis of influential environmental factors

Abstract

In the Chattooga River watershed, benthic macroinvertebrates surveys and habitat condition inventories have been conducted by the USFS annually since 1999 in headwater streams to better understand the responses of aquatic biota to sedimentation. Preliminary investigations using biological and habitat data obtained from those surveys revealed weak relationships between benthic macroinvertebrate assemblages, commonly used streambed sedimentation measures, and three sedimentation categories as part of random, multi-habitat composited macroinvertebrate samples (Chapter 1). In addition, a stratified, composited multi-habitat sample showed weak relationships (R2 = 0.101 - 0.322) with streambed substrate measurements associated with sedimentation (Chapter 2). Those findings suggest the need to investigate macroinvertebrate response to sedimentation at smaller spatial scales. In this study, we randomly collected

264 benthic macroinvertebrate samples along systematic transects in eight headwater mountain streams and recorded habitat conditions at those locations to summarize fauna and determine influential environmental factors. As expected, a high number of rare taxa were collected (55 of 96 taxa), with the fifteen dominant taxa comprising over 80% of the cumulative relative abundance among all samples. Those fifteen dominant taxa occurred over the full range of percent deposited sediment, but generally showed higher overall relative abundances among low percent deposited sediment levels (0 – 20%). Exploratory analysis using ordination revealed two axes explaining 62.1% of the community variation, with flow velocity the only axis-correlated environmental variable (R2 = .21). At a larger spatial scale, ordination using environmental variable averages and composited macroinvertebrate data per 12 m (4 samples per 12 m, n =

64) resulted in 25.2% of the community variation explained by two axes, with flow velocity and valley confinement having the greatest axes correlations. Overall findings show that (1) there are extremely ubiquitous dominant taxa, (2) high dominant-taxon abundances occur within low levels of percent deposited sediment, and (3) flow velocity potentially influences deposited sediment conditions and macroinvertebrate assemblages in these systems, with valley confinement correlated with an assemblage gradient produced from samples that were composited over 12 m.

Introduction

Sedimentation has become a growing concern in streams and rivers throughout the United States

(Waters 1995). In the southeastern U.S., historical accounts of destructive land-use activities have described streams as timber transport conduits (Dollof 1995), with such activities having persistent, negative influences on watersheds today (Harding et al. 1998). In the Chattooga River watershed (Georgia,

North Carolina, South Carolina, USA), sedimentation is a primary environmental concern that has recently been addressed by the USEPA (1999) for the enforcement of non-point source pollution legislation and efforts to establish effective sediment total maximum daily loads (TMDLs). Following the 1999 USEPA report, the USDA Forest Service (USFS) has implemented annual biological surveys and physical habitat assessments in order to inventory conditions and improve our understanding of sedimentation impacts to aquatic biota (Whalen et al. 2001, Roghair et al. 2002).

Benthic macroinvertebrates are excellent organisms for investigating sedimentation because of their strong association with substrate, flow conditions, and energy resources within streambed habitats

(Barbour et al. 1999), which are all environmental conditions that can be impacted when exposed to unnatural levels of deposited sediments. Within diverse macroinvertebrate assemblages, some organisms require clean substrate for feeding and reproduction and excessive sedimentation may disrupt normal behavior or remove some species from impacted habitats, thus changing the overall assemblage organization and potentially compromising normal stream function.

In headwater mountain streams, the habitat template is very heterogeneous, offering a wide range of macroinvertebrate habitats. Within those habitats, contrasting physical conditions persist along streambeds, leading to diverse and abundant macroinvertebrate assemblages. However, because of the contrasting environment along headwater streambeds, detecting links between the two is difficult and often involves various spatial scales. In a study comparing environmental influences at microhabitat, reach and basin spatial scales, Lamouroux and Doledec (2004) found 28% more significant relationships (out of 180 tests) between macroinvertebrate traits and environmental conditions at the microhabitat scale when compared to the reach scale.

USEPA (1999) listed streams in the Chattooga River watershed as threatened or impaired due to sedimentation using biological and physical assessments, and in several streams the biology was rated as

“good”, while the physical habitat was rated as “poor.” Using historical survey data collected by the USFS from the Chattooga River watershed and other headwater streams throughout the Chattahoochee National

Forest, we previously conducted an investigation of the relationships of benthic macroinvertebrates and streambed habitat variables associated with sedimentation. In that study, we found that very few macroinvertebrate assemblage metrics were related to sedimentation variables or different categories of sedimentation (i.e., low, medium, and high). Benthic macroinvertebrate sampling by the USFS consisted of a systematic longitudinal transect, random sampling design with transect sub-samples combined to form one benthic macroinvertebrate sample per stream. Therefore, because of the weak relationships found in our previous study and contrasting physical and biological conditions reported in other studies (Weber and

Isely 1995, USEPA 1999), we suggest the need to modify existing methods by reducing the scale at which benthic macroinvertebrate assemblages and their associated habitats are measured.

In this study, we used a systematic transect, random sampling design to collect a large number of benthic macroinvertebrate samples over eight, physically similar headwater streams. The sampling design was similar to that used by the USFS for biological surveys; however, we designated each transect a sample and recorded sample-specific streambed substrate variables at each transect. In so doing, we developed a large macroinvertebrate sample x environment matrix that could be used to investigate relationships at the sample scale. Our overall goal from this sampling design was to determine effective information in terms of specific macroinvertebrate sensitivity to sedimentation at the microhabitat scale, and to extrapolate that information to stream bioassessments.

Methods

Study area

The Chattooga River watershed lies at the southern end of the Appalachian Blue Ridge mountains

(Fig. 3-1). Approximately 70% of the watershed is in public ownership as National Forest. Mountain streams in the region are generally coldwater, high-gradient tributaries and catchment divides reach elevations of 1500 m. Steep landscapes contain a variety of vegetation including pines (Pinus virginiana and P. echinata) and oaks (Quercus prinus and Q. coccinea), with higher elevation streams canopied by white pine (Pinus strobus) and eastern hemlock (Tsuga canadensis), and riparian areas at those higher

elevations often containing dense understories of Rhododendron sp. Streams in the Chattooga River watershed are influenced by gneiss and shist rocks contained in loamy soils that are highly erodible and are primarily underlain by sedimentary and metamorphic bedrock (Van Lear 1995).

Streams selected for field sampling all had a previous history of disturbance due to timber harvests and related activities (Dollof 1995, Harding et al. 1998), but were all forested with mature stands. Only one watershed contained a road or portion thereof. Sites were selected to minimize the effects of contemporary watershed activities related to road-use and to isolate the mechanisms associated with the in- stream, micro-habitat scale effects of substrate environments on benthic macroinvertebrates. A detailed description of stream network characteristics in the Chattooga River watershed can be found in Hansen

(2001).

Field and laboratory methods

Eight streams were chosen for study in April 2003, with 6 located in Georgia, one in South

Carolina, and one in North Carolina (Fig. 3-1). Streams were selected to ensure that sites were well distributed across the Chattooga River watershed and represented typical headwater mountain streams found in the region. We used systematic, longitudinal sampling in order to cover the range of streambed habitats and associated deposited sediment levels. The systematic, longitudinal sampling design is essentially a multi-habitat design in that random sampling locations were selected along a longitudinal transect running parallel with the stream channel (Fig. 3-2) (Lazorchak et al. 1988). Samples were taken from a 100-m reach within each site. To randomize the beginning point (i.e., downstream end) of each reach, a random distance (range 0-100 m) was stepped upstream, with the designated reach beginning at the end of that distance. Within each 100-m reach, macroinvertebrates were systematically sampled at 33 locations spaced 3 m apart on the longitudinal transect. At each sampling location, a measuring tape was extended across the stream channel perpendicular to the longitudinal transect and a random number was chosen to locate the sample a specified distance in 0.1m increments from the left bank. A D-frame dip net

systematic and randomized sampling locations, a variety of substrate and current conditions were encountered. Therefore, the amount of time necessary to dislodge the organisms varied, but it generally ranged from 5 to 15 sec. Each macroinvertebrate sample was placed in a bag with 95% ethanol. In the laboratory, all macroinvertebrates were identified to the lowest possible taxonomic level, which was usually genus, using standard taxonomic references applicable to the region (Brigham and Brigham 1982,

Merrit and Cummins 1996, Wiggins 1996). The numbers of organisms in each taxon were counted and the data were stored in Excel spreadsheets until later analyses.

At each transect, percent deposited sediment was estimated to the nearest 5% by visually examining the streambed substrate content within a 0.5m2 metal frame placed upon the streambed (range =

0 – 100%). A value of 1% was recorded in the case where deposited sediment was less than 5% but greater than 0%. We defined percent deposited sediment as substrate sizes < 2mm (i.e., sand, silt, clay). One value of percent deposited sediment was recorded at each macroinvertebrate sampling location. Dominant and subdominant substrate were recorded within the metal frame and assigned one of the following numbers: bedrock = 9, boulder = 8, cobble = 7, large gravel = 6, small gravel = 5, sand = 4, silt = 3, clay =

2, and 1 = detritus. Dominant substrate was the substrate type occurring over the majority of the metal frame area, with subdominant substrate be the second substrate type with the most streambed area covered within the frame. Flow velocity (Marsh – McBirney® flow meter) and water depth were recorded from just within the front edge of the metal frame. In addition to the sample scale environmental variables recorded at each transect, at every 12 m (four-transect composite) we measured channel gradient with a clinometer and estimated stream valley confinement as the ratio of flood-prone width to bankfull width.

Data analysis

We summarized benthic macroinvertebrate abundances over all samples (n = 264) using absolute abundances, relative abundances, and sample frequencies. Rare taxa were categorized as occurring in less than 10% of the samples and very rare taxa were categorized as occurring in less than 10% of the samples and in only one stream. Frequencies of percent deposited sediment, dominant substrate, and subdominant substrate were plotted graphically to show the distribution of those environmental variables among samples, and Spearman correlations coefficients were calculated to investigate relationships among those environmental variables. Using the fifteen dominant taxa occurring over all samples, we plotted relative

abundances per deposited sediment intervals to view the distribution of those dominant taxa over the entire percent deposited sediment range. In addition, Spearman correlation coefficients were calculated for all environmental variables using Sigma Stat version 3 (SPSS Inc., Chicago, IL)

A series of ordinations were conducted to examine assemblage patterns and to determine if environmental variables were related to those patterns. We used detrended correspondence analysis (DCA) with environmental overlays for ordinations because we wanted to first determine if assemblage gradients were present in our dataset and second if environmental variables were correlated with explanatory axes in the ordination. The main matrices for ordinations involved a 264 sample x 64 taxa main matrix and a 264 sample x 6 environmental variable second matrix. The influence of dominant substrate on assemblages was assessed independently of other environmental variables with DCA by using a second matrix with dominant substrate as the group label. Additionally, to determine if channel gradient and stream valley confinement were related to assemblage patterns and environmental averages over 12 m, we composited macroinvertebrate data for every 12 m (four transects composited) to yield 64 samples and then ran DCA.

Macroinvertebrate data was log(x+1) transformed, rare taxa were removed (occurring in less than 10% of samples), and environmental variables were relativized to standard deviates prior to ordinations. DCA was performed using PC-ORD version 4 (MjM software, Glenedon Beach, OR).

Results

We recorded a total of 96 taxa among 16,694 individuals, with 55 taxa being rare and 19 being very rare (occurring in only one stream) (Table 3-1). Orthocladiinae and Ephemerella sp. were the most frequently collected taxa among samples with 222 and 217 occurrences, respectively (Table 3-2). Those numbers indicate that Orthocladiinae occurred at 84% and Ephemerella sp. occurred at 82% of the sampling locations. The next most frequent taxa were Tanypodinae (165, 62%) and Paraleptophlebia sp.

(161, 61%). The fifteen dominant taxa occurred in all eight streams and comprised 80% of the overall cumulative relative abundance, with three individual taxon relative abundances greater than 10%:

Orthocladiinae (13.7%), Ephemerella (11.6%), and Leuctra (10.4%) (Table 3-2). Over half of the total macroinvertebrate abundance over all samples was attributable to the top six dominant taxa.

Mean values of width, depth, and flow velocity among all samples is shown in Table 3-3. Width showed the least variation among samples (CV = 32.1%), with a range of 1.2 – 6.5m. Depth and flow velocity were more variable with high CVs, likely due to the different habitat types present along a longitudinal reach. Mean percent deposited sediment was 0.33, but was highly variable among samples

(CV = 97%), probably due to the patchy spatial distribution of coarse sands in these systems. Spearman correlation coefficients (Sr) among environmental variables reveal several significant relationships (Table

3-4). Flow velocity was negatively related to percent deposited sediment (Sr = -.52), showed a positive relationships with dominant substrate (Sr = .42), and was weakly related to subdominant substrate (Sr =

.17). Percent deposited sediment is shown to be significantly and negatively correlated with dominant substrate (Sr = -.67). However, that relationship was expected because our measure of percent deposited sediment at each sampling location was a measure of fines less than 2mm, which in our streams was essentially shown to be a measure of sand substrate (reported below). Therefore, increasing levels of percent deposited sediment at a sample locations would result in a decrease in streambed covered by other, larger substrate. Stream channel width was negatively associated with flow velocity (Sr = -.22) and dominant substrate (Sr = -.14), which suggests that as channels become narrow, flow velocity increases and dominant substrate size increases. Those physical conditions, such as a narrow boulder chutes, are typical for streams in the region. The weak, but significant relationships of those types of habitats may be due to the low frequency of occurrence over our random sample range.

Cobble was the dominant substrate among samples, with sand being the second most frequent (Fig

3-3). Sand, small gravel, and large gravel were the most frequent subdominant substrate types recorded

(Fig 3-4). Occurrences of dominant substrate and subdominant substrate indicate that streams are primarily comprised of cobble-sand streambeds. From total substrate frequencies, we determined that sand as dominant substrate mostly occurred with the subdominant substrate small gravel. Likewise, sand as subdominant substrate usually was found to occur with small gravel as the dominant substrate.

The frequency of percent deposited sediment occurrence shows that most samples were associated with deposited sediment levels ranging from 0 – 20% (Fig. 3-5). The frequency of individual percent deposited sediment levels at 5% increments from 30 – 100% was low at ≈12. However, with a frequency

of ≈12 for those samples, the combined frequency of 30 – 100% deposited sediment occurrence would be

≈170 (15 percentage increments from 30 – 100% multiplied by 12).

The distribution of the fifteen dominant taxa over the deposited sediment range is shown with overall relative abundances per deposited sediment level (Fig. 3-6). All taxa show higher relative abundances within the low percent deposited sediment levels (< 25%) when compared to moderate (≈30 –

50%) and high percent deposited sediment (≈50 – 100%). Ephemerella sp., Oulimnius latisulcus, Baetis sp. (complex), Optioservis sp., Tallaperla sp., Diplectrona modesta, Tanytarsini, and Hexatoma sp. all show higher relative abundances associated with low percent deposited values and a leveling or tapering off of relative abundance as deposited sediment increases. Chironominae, Amphinemura sp., Oligochaeta,

Orthocladiinae, Leuctra sp., Paraleptophlebia sp., and Tanypodinae have higher relative abundances at low percent deposited sediment, but relative abundances within a particular high level of deposited sediment sharply increases at the 60% level (Fig 3-6). The chironomids Tanypodinae, Orthocladiinae, and

Chironominae generally show higher relative abundances at low deposited sediments, paucity in relative abundance in the middle deposited sediment range, and increasing relative abundances from the mid to high levels of percent deposited sediment (Fig. 3-6).

Following the removal of rare taxa that occurred in less than 10% of the samples (27 samples), our main matrix used for DCA ordinations consisted of 264 sample x 37 taxa. The first ordination analysis was performed using dominant substrate type as the sample label (Fig. 3-7). Because sand was found to be the second most frequent dominant substrate type, determining if distinct assemblages existed in that substrate would provide insight into assemblage changes due to increasing, reach scale sedimentation due to coarse sands. That analysis produced an ordination explaining 62.1% percent of the assemblage variation. Axis 1 explained 21.9 and axis 2 40.2% of the assemblage variation among samples. The influence of dominant substrate on the sample ordination was assessed with an environmental overlay. That overlay of dominant substrate revealed a weak R2 coefficient with axis 2 (R2 = .144). From observing the DCA ordination graph (Fig. 3-7), a slight gradient in dominant substrate was observed, mainly due to bedrock samples in the upper left portion of the graph. There was also a slight gradient shown by the sand dominant substrate samples (light blue symbols) toward the negative end of axis 1. In the main ordination, three taxa were correlated with axis 1 and one taxon was correlated with axis 2. All four taxa showed a patterned, but

vague in most cases, distribution among dominant substrate types (Figs. 3-8 through 3-11). The taxon with the highest correlation with the main macroinvertebrate sample gradient was Ephemera sp. (R2 = .41 with axis 1 (Fig. 3-8). Amphinemura sp. had an R2 value of 0.36 with axis 1 and showed relatively higher abundances in non-sand substrate (Fig. 3-9). Tallaperla sp. showed a similar pattern with an R2 of 0.32 for axis 1 (Fig 3-10). Epeorus sp. had the highest correlation with axis 2 (R2 = .35), and was mainly associated with bedrock substrate (Fig. 3-11).

A second series of ordinations were performed to investigate the full suite of environmental variables and macroinvertebrate assemblage patterns. Results of that analysis produced an ordination that explained the same percent variance in assemblage data as the ordination above used to investigate dominant substrate influences (61.9% total variance explained on two axes). The only environmental variable with an R2 above 0.20 was flow velocity (R2 = .21), which was correlated with axis 2 that explained most of the assemblage variation (40.2%, Fig. 3-12). Examination of the symbol plot for flow velocity showed a few strong values located in the upper left quadrant of the graph that probably strengthened the R2 value (Fig 3-13). However, the symbol plot supports the moderate correlation of flow velocity with axis 2. A symbol plot of deposited sediment among samples shows high overlap of percent deposited sediment values (Fig. 3-14) and no correlation of deposited sediment with either axes in the sample ordination. However, the symbol plot reveals a very slight gradient from the bottom right to the top left quadrant, a pattern opposite to that of flow velocity in Figure 3-11.

The dataset produced by compositing macroinvertebrate samples every 12 m (n = 64 samples) contained 64 taxa after removing rare taxa occurring in less than 10% of the samples (7 samples).

Therefore, ordination analysis was performed using a 64 sample x 64 taxa main matrix and an environmental matrix made up of the same sample-scale environmental variables, but the environmental variables in the 12-m composited dataset were environmental averages over the 4 transects contained in the

12 m. In addition, the variables percent gradient and stream channel confinement were added to the environmental variable second matrix to yield a total of 8 environmental variables. Using those matrices,

DCA produced an ordination showing strong outliers due to four samples (Fig 3-15) and no correlations of environmental variables with the explanatory axes. Removal of those outliers produced an ordination that moderately explained assemblage variation (26%), with correlations of flow velocity with axis 2 (R2 =

0.26) and valley confinement with axis 1 (R2 = 0.24, Fig 3-16). Cumulative percent variance explained with the four outliers was 48.2%, while removal of those outliers resulted in a reduction in cumulative percent sample variance explained (25.2%) but with correlations of flow and valley confinement on those axes.

Discussion

The systematic, longitudinal transect sampling design we used resulted in collecting many rare taxa. Several rare taxa removal criteria are used in analyzing macroinvertebrate assemblage patterns.

Commonly used methods include (1) removal of species that occur as <0.5% of the sample abundance, (2) removal of taxa occurring in less than n samples, and (3) taxa with fewer than n individuals. An additional method is to remove taxa that contain fewer than n non-zero values, as implemented in PC-ORD. With some methods, caution must be taken to avoid removing important taxa that occur as highly abundant singletons (i.e., only 1 occurrence over the entire sample). Because we collected a large number of benthic macroinvertebrate samples, we chose to remove rare taxa that occurred in less than 10% of the samples.

We identified 16 taxa occurring in only 1 sample, of which no taxa had over four individuals. None of the rare taxa or very rare taxa had raw abundances over 40 or relative abundances exceeding 0.2%. Overall,

57% of the taxa we identified were considered rare (54 of 96 taxa) . In one of the few studies we found that employed a systematic, random sampling design, Snyder et al. (2002) identified 64 rare taxa (42%) occurring at fewer than 4 sites in Delaware streams. Roy et al. (2003) eliminated 24 rare taxa, out of 91 total taxa, as a result of sampling riffle, pool, and bank habitats in streams located in north-central Georgia.

Although the multi-habitat approach used by Roy et al. (2003) includes sampling in a variety of habitats over a relatively large streambed area, sampling is stratified nonetheless, thus leading to a lower percentage of rare taxa when compared to a systematic, random multi-habitat sampling design. In addition, sampling conducted along the longitudinal transects resulted in a total reach-scale, streambed area sampled of

2.97m2, which is slightly higher than the average of 1.7m2 reported in a review of sampling methods used by U.S. State agencies (Carter and Resh 2001).

The fifteen dominant taxa among samples comprised about 80% of our macroinvertebrate dataset

(13,372 out of 16, 694), which shows high ubiquity among those taxa across samples in our eight streams.

The three most dominant taxa were Orthocladiinae (Diptera; Chironomidae), Ephemerella sp.

(Ephemeroptera; Ephemerellidae), and Leuctra sp. (Plecoptera; Leuctridae). The Orthocladiinae are collector-gatherers that burrow into soft sediments and are typically associated with small rock and gravel substrata (Pinder 1986), thus these streams comprised of a portion of coarse sand among gravel patches facilitates high numbers of that group. Ephemerella sp. is a collector-gatherer associated with the crawler habit group, which indicates that it spends time crawling upon the streambed while generally gathering non-specific food resources. Leuctra sp. is a shredder and crawler, which is an important functional feeding group in headwater, forested streams that rely on leaf inputs to maintain a heterotrophic system.

Those three species occurred in ≥ 74% of the 264 samples. Those findings, including their ecological partitioning associated with their respective functional feeding and habitat traits, suggest that those taxa are highly adapted to these headwater streams containing a portion of coarse sand upon the streambed. In addition, the dominance of those taxa, especially the crawler habit group that requires clean, unobstructed substrate, raises the important question of bedload movements associated with coarse sands in these systems. An important future study should include investigating the role of sand habitats as organic matter repositories and physical areas that allow crawling and gathering of food resources in these headwater systems.

Environmental variation across our sample was high. However, that variation was expected due to the random, systematic sampling among multiple streambed habitats. We report the overall statistics on those variables in order to show the high sample variation and for reference of future studies using similar- size streams in the region. The most noteworthy correlations of environment variables was that of flow velocity with percent deposited sediment and dominant substrate. That finding suggests deposited sediments in these headwater systems may depend primarily on fluvial processes shaping streambed habitats, with impact to biota dependent on flow-induced habitat types and conditions.

We found sand to be the second most frequent substrate type encountered among samples. That relative occurrence, produced from the random sampling design, indicates that these headwater systems maintain a large portion of sand in the streambeds. Moreover, sand mostly occurred in the presence of small gravel substrate, either as subdominant or dominant substrate. The importance of that sand as a component of bedload sedimentation in these headwater systems depends on its static or dynamic nature,

something that has not been investigated in these headwater systems. In riverine environments, moving bed materials can be inhabited but usually contain organisms an order of magnitude smaller than those found in larger substrate types (Whitman and Clark 1984) and that are short-lived, therefore requiring bed stability longer than a life cycle (Peeters and Tachet 1989). We found no taxa to be exclusive to habitats dominated by sand. Therefore, we could not relate organism traits to that habitat. However, the occurrence of a relatively few dominant taxa suggests the need for maintaining a large portion (based on high sample frequencies of dominant taxa) of substrate with low amounts of deposited coarse sands.

Figure 3-17 shows three levels of streambed compaction due to sedimentation by coarse sand. As can be seen in the second and third (from left) pictures, the streambed contains a large portion of sand, especially in the vertical profile. Although there are high sand volumes and few cobble particles present in those pictures, that type of streambed may be providing adequate room and clear surface area for movement. Headwater streams in the Chattooga River watershed appear to be mostly compacted as in the second and third picture. Substrate conditions in the leftmost picture may be found in some streams, but occurs much less frequently and only in positions that do not allow inputs of fine sediments due to water flow or geomorphic or other physical obstructions that do not allow entrance of sediments.

Although the largest relative abundances among the fifteen dominant taxa were associated with low levels of deposited sediment, a few taxa showed sharp increases towards the high deposited sediment percentages. Three taxa all showed a sharp spike in overall relative abundance at the 60% level of deposited sediment (Fig. 3-6). Because of that finding, we wanted to determine which increases in relative abundances were related to each other. By determining the number of samples associated with that deposited sediment level and calculating the macroinvertebrate abundance per sample, we found that all three taxa were attributed to one sample (OF14). In that one sample, there were 179 Leuctra sp., 75

Paraleptophlebia sp., and 45 Amphinemura sp. The environmental variables attributable to OF14 showed detritus/organic material as the dominant substrate with a 60% level of deposited sediment and 0.29 m/s flow velocity. That finding suggests particulate retention due to both the detritus dominant substrate and high percent deposited sediment, and favorability to over 300 individuals.

Flow velocity was the only environmental variable that was consistently correlated with ordination axes produced by the sample-scale dataset and the 12 m composited macroinvertebrate dataset. In addition,

compositing the macroinvertebrate data over 12 m resulted in four very strong outliers from the same stream. However, those outliers were not detected in the sample-scale ordination. After viewing field notes, we found that near the upper end of the 100 m longitudinal reach in Slatten Branch, there was a very recent, massive bank failure on the left side of the channel that had uprooted a tree. The tree crown had partially blocked the stream channel and there was a low to moderate accumulation of sediment at the edge of the stream channel. The number of taxa from each of those outlier sections associated with the felled tree was similar to all remaining samples. However, further examination found those sites to have high numbers of the blackfly, Simulium sp. (91 individuals), which contributed to the outlying samples. The most frequent dominant substrate at those locations was cobble and large gravel that apparently facilitated blackfly attachment to substrate. Those unusually high numbers suggest that the tree was providing fine organic material or that the removal of the tree canopy provided light and subsequent organic growth in the stream. Erosion from the exposed rootwad, streambank, or in-stream erosion of organic material from the tree itself may have been providing organic material for blackfly filtering. Conversely, the disturbance to the streambed caused by the fallen tree may have displaced local resident blackfly individuals that drifted to nearby downstream sections. In any case, the high numbers of blackflies in those outlier samples were anomalous among all remaining samples in our study streams.

Overall findings from this study show that, along our eight streambeds, there is a relatively consistent and dominant benthic macroinvertebrate fauna. Although a number of rare taxa were encountered, a short list of taxa make up most of the assemblage and may be the main providers of ecological function in these systems at intermediate trophic levels. Flow velocity appears to be the key environmental factor shaping habitats and influencing benthic macroinvertebrate patterns in these systems.

Therefore, studies investigating the impacts of sedimentation in these systems must consider potential differences in deposited sediment impacts to biota due to differences in flow-mediated habitat types.

Acknowledgements

We thank Craig Roghair and Dan Nuckols of the USFS Center for Aquatic Technology Transfer and Jamie

Roberts for assistance in data collection. We are grateful to Stephen Hiner, Amanda Ellis, Trisha Voshell,

Rachel Wade, and Kathy Hanna for laboratory assistance. Thanks to Charlene Breeden of the USFS

Tallulah Ranger District in Georgia for providing resources in the Chattahoochee National Forest.

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Table 3-1 Benthic macroinvertebrate total abundance and relative abundance from 264 samples across eight streams in the Chattooga River watershed. Thirty-three random benthic macroinvertebrate samples were collected at 33 systematic transects (spaced 3m apart) across the eight streams (n = 264). Relative abundances are calculated from the total number of organisms collected (16, 694). Sample frequency is the number of transects that a particular taxon was collected out of the 264 samples.

total relative sample number abundance frequency of individuals (%) (n = 264) NON-INSECTA Oligochaeta 309 1.9 108 Collembola 102 0.6 53 Decapoda CambaridaeR 21 0.1 19 Bivalvia CorbiculaR 28 0.2 14

Branchiobdellida 4 0.0 2 HirudineaRR 2 0.0 2 HydracarinaRR 1 0.0 1 Amphipoda GammaridaeRR 1 0.0 1

INSECTA Ephemeroptera Ameletidae Ameletus 98 0.6 60 Baetidae Baetis (complex) 454 2.7 141 Ephemerellidae Ephemerella 1916 11.5 217 EurylophellaR 28 0.2 10 SerratellaRR 2 0.0 1 Ephemeridae Ephemera 94 0.6 33 Heptageniidae Stenonema 204 1.2 91 Epeorus 106 0.6 47 Leucrocuta 54 0.3 35 Cinygmula subaequalisR 5 0.0 4 Isonychiidae Isonychia 67 0.4 31 Leptophlebiidae Paraleptophlebia 1136 6.8 161 Habrophlebia vibrans 86 0.5 34 HabrophleboidesR 14 0.1 2 Siphlonuridae SiphlonurusRR 2 0.0 1 Odonata Aeshnidae BoyeriaRR 1 0.0 1 Calopterygidae CalopteryxRR 1 0.0 1 Cordulegastridae Cordulegaster 87 0.5 53 Gomphidae Lanthus 181 1.1 93 GomphusRR 9 0.1 7 StylogomphusRR 1 0.0 1 Plecoptera Chloroperlidae SweltsaR 21 0.1 19

HaploperlaR 27 0.2 16 SuwalliaR 13 0.1 11 Isoperlidae IsoperlaR 29 0.2 22 Leuctridae Leuctra 1722 10.3 194 Nemouridae Amphinemura 560 3.4 113 Perlidae Acroneuria 67 0.4 28 Eccoptura xanthenesR 7 0.0 7 PerlestaRR 3 0.0 1 Peltoperlidae Tallaperla 656 3.9 111 Perlodidae Remenus 102 0.6 56 Yugus 83 0.5 38 Pteronarcyidae Pteronarcys 41 0.2 26 Taeniopterygidae TaeniopteryxRR 4 0.0 1 Megaloptera Corydalidae Nigronia fasciatusR 60 0.4 13 Trichoptera Glossosomatidae Glossosoma 102 0.6 39 AgapetusR 32 0.2 20 Hydropsychidae Diplectrona modesta 613 3.7 139 ParapsycheRR 1 0.0 1 Hydroptilidae HydroptilaRR 2 0.0 2 Lepidostomatidae Lepidostoma 60 0.4 41 Limnephilidae PycnopsycheR 35 0.2 19 PseudolimnephilaRR 2 0.0 1 Molanidae MolannaR 8 0.0 7 Odontoceridae PsilotretaR 8 0.0 6 Philopotamidae Dolophilodes distinctus 122 0.7 47 WormaldiaR 9 0.1 6 Polycentropidae Polycentropus 79 0.5 50 PhylocentropusR 5 0.0 3 CyrnellusR 3 0.0 2 Psychomyiidae Lype diversaR 19 0.1 17 Rhyacophilidae Rhyacophila 149 0.9 83 Sericostomatidae Fattigia peleR 5 0.0 5 Uenoidae NeophylaxR 29 0.2 20 Coleoptera Elmidae Oulimnius latiusculus 690 4.1 152 Optioservus 443 2.7 123 PromoresiaR 57 0.3 21 StenelmisR 6 0.0 6 MacronychusR 6 0.0 5 Gyrinidae GyrinusRR 1 0.0 1 Psephenidae EctopriaR 40 0.2 21 Psephenus herrickiR 18 0.1 15 Ptilodactylidae AnchytarsusR 40 0.2 21 Diptera Athericidae AtherixR 3 0.0 3 Ceratopogonidae 142 0.9 84

Chironomidae Orthocaldiinae 2274 13.6 222 Tanypodinae 749 4.5 165 Chironominae 685 4.1 154 Tanytarsini 589 3.5 144 DiamesinaeR 35 0.2 11 Dixidae DixaR 19 0.1 12 Empididae HemerodromiaR 20 0.1 17 CheliferaR 6 0.0 6 OreogetonR 3 0.0 3 ClinoceraRR 1 0.0 1 Limnophilidae LimnophilaRR 4 0.0 1 Nymphomyiidae NymphomyiaR 3 0.0 3 Psychodidae PericomaR 2 0.0 2 Simuliidae Simulium 165 1.0 53 Prosimulium 114 0.7 49 Tabanidae ChrysopsRR 1 0.0 1 Tipulidae Hexatoma 576 3.5 141 Dicranota 145 0.9 78 Tipula 137 0.8 57 AntochaR 27 0.2 13 EriopteraRR 1 0.0 1 R occurring in less than 10% of the samples RR very rare, occurring in less than 10% of the samples and in only one stream

Table 3-2 Summary of the fifteen dominant macroinvertebrate taxa occurring among all samples (N = 264) and among eight streams. Frequency is the number of times the insect was collected among samples and streams. Total number of organisms across all streams was 16, 694. CR = crawler, BU = Burrower, CLG = clinger, PR = predator, SH = shredder, CG = collector-gatherer, SC = scraper, CF = collector- filterer.

cumulative total relative relative number of sample stream Taxon abundance (%) abundance (%) individuals frequency frequency ffg habit Orthocladinae 13.7 13.7 2274 222 8 CG BU Ephemerella 11.6 25.3 1916 217 8 CG CR Leuctra 10.4 35.7 1722 194 8 SH CR Paraleptophlebia 6.9 42.6 1136 161 8 CG CR Tanypodinae 4.5 47.1 749 165 8 PR SP Oulimnius latiusculus 4.2 51.2 690 152 8 SC CLG Chironominae 4.1 55.4 685 154 8 CG BU Tallaperla 4.0 59.3 656 111 8 SH CR Diplectrona modesta 3.7 63.0 613 139 8 CF CLG Tanytarsini 3.6 66.6 589 144 8 CF CLG Hexatoma 3.5 70.1 576 141 8 PR CR Amphinemura 3.4 73.5 560 113 8 SH CR Baetis (complex) 2.7 76.2 454 141 8 CG CLG Optioservus 2.7 78.9 443 123 8 SC CLG Oligochaeta 1.9 80.7 309 108 8 CG BU

Table 3-3 Environmental variables and statistics for 264 sample collection sites across eight Chattooga

River headwater streams.

variable mean (s.d.) CV range stream channel width (m) 3.05 (.98) 32.1 1.2 - 6.5

water depth (cm) 12.85 (9.70) 75.5 1 - 85 flow velocity (m/s) 0.27 (.28) 97.0 0 - 2 percent deposited sediment 0.33 (.32) 97.0 0 - 100%

Table 3-4 Spearman rank correlations of environmental variables collected at 264 macroinvertebrate sampling locations in eight Chattooga River headwater streams (n = 264), with significant relationships shown in bold and P-values in parentheses (α = 0.05).

depth (cm) flow (m/sec) % dep sed dom. sub subdom. sub width (m) -0.06 -0.22 0.15 -0.14 -0.03

(0.341) (0.000) (0.013) (0.020) (0.644)

depth (cm) -0.01 0.10 0.00 0.06 (0.844) (0.102) (0.991) (0.366)

flow (m/sec) -0.52 0.42 0.17 (0.000) (0.000) (0.007)

% dep sed -0.67 -0.23 (0.000) (0.000)

dom. sub -0.01 (0.902)

Fig. 3-1 Map of the southeastern U.S. showing the location of the Chattooga River watershed and the smaller subwatersheds containing our study sites.

Overflow Branch tributary (OF)

Slatten Branch (SL) Holcomb Creek tributary (HC) Bailey Branch (BA) Harden Creek Martin Creek (MA) Tributary (HR)

Goldmine Branch (GM)

Lick Log (LL)

Fig. 3-2 Systematic longitudinal transect sampling design used in eight headwater streams in the

Chattooga River watershed (A), showing random locations of macroinvertebrate sampling locations (x) at each transect. Thirty-three transects per stream were spaced 3 m apart (dashed lines, five transects shown).

The relative stream channel profile among 100 m reaches in the eight study streams is shown in B. Stream names are given with abbreviations in Figure 3.2.

x x

100.0

99.0 GM 98.0 MA

97.0 HC LL

96.0 BA

95.0 OF SL 94.0

Assumed Datum m, Elevation, 93.0 HR 92.0 0612 18 24 30 36 42 48 54 60 66 72 78 84 90 96

Distance, m

Fig. 3-3 Frequencies of dominant substrate types among all samples collected across eight Chattooga River headwater streams. One dominant substrate type was recorded at each benthic macroinvertebrate sampling location (n = 264).

cobble

sand

small gravel

bedrock

large gravel boulder type substrate dominant detritus/organic

silt

0 102030405060708090 number of dominant substrate occurrences

Fig. 3-4 Frequencies of sub-dominant substrate types among all samples collected across eight Chattooga River headwater streams. One sub-dominant substrate type was recorded at each benthic macroinvertebrate sampling location (n = 264).

sand

small gravel large gravel

detritus/organic

cobble

silt

type substrate sub-dominant bedrock boulder

0 10203040506070 number of sub-dominant substrate occurrences

Fig. 3-5 Frequency of deposited sedimentation percentage recorded at each benthic macroinvertebrate sampling location (transect). At each transect, percent deposited sediment was visually estimated within a

.5 m2 frame placed upon the streambed (n = 264).

of number samples 15

0 0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 percent deposited sediment

Fig. 3-6 Overall relative abundance of the 15 dominant taxa per deposited sediment level (%DS). Relative abundances on y-axes vary among taxa.

0.18 0.30 0.16 Orthocladiinae Ephemerella sp. 0.25 0.14 0.12 0.20 0.10 0.15 0.08 0.06 0.10 0.04 abundance relative abundance relative 0.05 0.02 0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 %DS %DS

0.16 Leuctra sp. 0.18 Paraleptophlebia sp. 0.14 0.16 0.12 0.14 0.12 0.10 0.10 0.08 0.08 0.06 0.06 0.04 0.04 relative abundance relative abundance relative 0.02 0.02 0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 %DS %DS

0.18 0.35 0.16 Tanypodinae Oulimnius latisulcus 0.30 0.14 0.25 0.12 0.10 0.20 0.08 0.15

0.06 0.10 relative abundance relative

relative abundance 0.04 0.05 0.02 0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 %DS %DS

Fig. 3-6 continued.

0.18 0.25 0.16 Chironominae Tallaperla sp. 0.14 0.20 0.12 0.15 0.10 0.08 0.10 0.06 0.04 relative abundance relative abundance relative 0.05 0.02 0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 %DS %DS

0.30 Diplectrona modesta 0.30 Tanytarsini

0.25 0.25 0.20 0.20 0.15 0.15

0.10 0.10 relative abundance relative 0.05 abundance relative 0.05

0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 %DS %DS

0.16 0.35 Hexatoma sp. 0.14 Amphinemura sp. 0.30 0.12 0.25 0.10 0.20 0.08 0.15 0.06 0.10 0.04 relative abundance relative abundance relative 0.05 0.02

0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 % DS % DS

Fig. 3-6 continued.

0.30 0.30 Baetis (complex) Optioservis sp. 0.25 0.25 0.20 0.20 0.15 0.15

0.10 0.10

relative abundance relative abundance relative 0.05 0.05 0.00 0.00 0 5 15 25 35 45 55 65 75 85 95 0 5 15 25 35 45 55 65 75 85 95 % DS % DS

0.30 Oligochaeta 0.25

0.20

0.15

0.10 relative abundance 0.05 0.00 0 5 15 25 35 45 55 65 75 85 95 % DS

Fig. 3-7 Results of detrended correspondence analysis ordination produced from a 264 sample x 37 taxa main matrix developed from macroinvertebrate samples collected in the Chattooga River watershed. An overlay of the environmental variable dominant substrate showed a weak relationship with axis 2 (R2 =

.14). Total cumulative percent variance explained along axis 1 and 2 was 62.1%.

dominant substrate organic / detritus silt sand small gravel large gravel cobble boulder bedrock Axis 2 (40.2%) Axis

Axis 1 (21.9%)

Fig. 3-8 Results of detrended correspondence analysis with Ephemera sp. represented by a symbol plot.

Ephemera sp. is distributed among substrate types at the right end of axis 1 and showed an R2 value of 0.41 with that axis but was variable in its association with a specific dominant substrate.

dominant substrate organic / detritus silt sand small gravel large gravel cobble boulder bedrock Axis 2 (40.2%) Axis

0.0 0.4 0.8 1.2 Axis 1 (21.9%)

Ephemera 1.2 Axis 1 (21.9%) r = .636 tau = .424 Axis 2 (40.2%) r = -.045 tau = -.068 0.8

0.4

0.0

100

Fig. 3-9 Results of detrended correspondence analysis with Amphinemura sp. represented by a symbol plot. Amphinemura is distributed among substrate types at the right end of axis 1 and showed an R2 value of 0.35 with that axis. Note the grouping of light blue symbols representing sand dominant substrate type that are opposite of samples with high numbers of Amphinemura.

dominant substrate organic / detritus silt sand small gravel large gravel cobble boulder bedrock Axis 2 (40.2%) Axis

0.0 0.5 1.0 1.5 2.0 Axis 1 (21.9%)

Amphinemura 2.0

Axis 1 (21.9%) r = -.579 tau = -.510 1.5 Axis 2 (40.2%) r = -.261 tau = -.176 1.0

0.5

0.0

101

Fig. 3-10 Results of detrended correspondence analysis with Tallaperla sp. represented by a symbol plot.

Tallaperla sp. is distributed among substrate types at the right end of axis 1 and showed an R2 value of 0.46 with that axis. Note the grouping of light blue symbols representing sand dominant substrate type that are opposite of samples with high abundances of Tallaperla.

dominant substrate organic / detritus silt sand small gravel large gravel cobble boulder bedrock Axis 2 (40.2%) Axis

0.0 0.5 1.0 1.5 2.0 Axis 1 (21.9%)

Tallaperla 2.0

Axis 1 (21.9%) r = -.568 tau = -.484 1.5 Axis 2 (40.2%) r = -.083 tau = -.099 1.0

0.5

0.0

102

Fig. 3-11 Results of detrended correspondence analysis with Epeorus sp. represented by a symbol plot.

Epeorus sp. is distributed among substrate types at along axis two (R2 = .34) and is shown to be associated mostly with bedrock and cobble dominant substrate.

dominant substrate organic / detritus silt sand small gravel large gravel cobble boulder bedrock Axis 2 (40.2%) Axis

0.0 0.4 0.8 Axis 1 (21.9%)

Epeorus

Axis 1 (21.9%) 0.8 r = -.173 tau = -.158 Axis 2 (40.2%) r = .588 tau = .433

0.4

0.0

103

Fig. 3-12 Results of detrended correspondence analysis showing flow velocity as the only environmental variable correlated with axis 2. Percent of variance explained in assemblage data for both axes is 62.1%.

flow velocity

Axis 2 (40.2%) (40.2%) 2 Axis

Axis 1 (21.9%)

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Fig. 3-13 Results of detrended correspondence analysis for the variable flow velocity using a 264 site x 37 species main matrix and a 264 site x 6 variable second matrix. The coordinate graph shows the distribution of sites (+), with the larger symbols indicating higher flow velocities.

Axis 2

Axis 1

0.0 0.5 1.0 1.5 2.0 flow (m/sec) 2.0

Axis 1 r = -.235 tau = -.205 1.5 Axis 2 r = .472 tau = .269 1.0

0.5

0.0

105

Fig. 3-14 Results of detrended correspondence analysis for the variable percent deposited sediment using a

264 site x 37 species main matrix and a 264 site x 6 variable second matrix. The ordination graph shows the distribution of sites (+), with the range of deposited sediment values indicated by the relative symbol size. One visually estimated, deposited sediment percentage was associated with each benthic macroinvertebrate sampling location. Axis 2

Axis 1

0.0 0.4 0.8 dep sed

Axis 1 0.8 r = .384 tau = .272 Axis 2 r = -.215 tau = -.230

0.4

0.0

106

Fig. 3-15 Results of detrended correspondence for the 64 sample x 64 species main matrix. Four sample outliers are shown with arrow. Each sample plot is a composite of four samples over a 12m longitudinal distance.

SL72

SL96

SL84

SL60

2 (48.2%) Axis

GM24 GM12 GM96 GM36GM48 LL12 LL36 GM84 OF60 OF24OF12 HR36BA72SL24 HC24 LL48 LL84LL96 GM72 OF96 LL72GM60 MA72 HR12 BA84 LL60 BA96 SL12OF48HC84 SL36HC36 OF84HR24HR96HR60 HC12 MA36MA84 BA36 SL48HC72OF36BA60HC60 BA12MA12 HC96 LL24HR48 HC48 MA24 MA96HR72 BA48 HR84 MA60 BA24 MA48OF72

Axis 1 (.08%)

107

Fig. 3-16 Results of detrended correspondence analysis of 12m composited samples with four outliers removed. Overlay of environmental variables with axes shows correlation of flow velocity with axis 2 and valley confinement with axis 1. The two axes explain 25.2% of assemblage variation.

OF72 LL36

OF84 LL48 HR96 LL24 LL60 MA72 LL72 OF12BA72 OF24 BA84 MA96 MA4 8 BA96 flow velocity OF36 HR36 LL84

OF96 HR72 MA12 MA24 OF60 MA36 BA12 LL12 LL96 HR12 HR60 valley confinement Axis 2 (11.5%) Axis BA24 HR48 GM60 MA8 4 SL12 MA60 HC48 BA60 BA48 HC60 HC84 GM84 HC36 HC72 SL24OF48 HR24 HR84 GM96

SL36 SL48 GM36 HC24HC96 BA36

GM12GM24 HC12 GM48 GM72

Axis 1 (13.7%)

108

Fig. 3-17 Profile streambed showing substrate distribution and the relative proportions of large gravel, small gravel, and sand. Image modified from Platts et al. (1983).

top view

side view

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CHAPTER 4

Benthic macroinvertebrate relationships with deposited sediments in fast- and slow-water, mountain

stream habitats

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Abstract

A previous study that evaluated the effects of sedimentation on benthic macroinvertebrates in

Chattooga River headwater streams (Chapter 3) revealed that the predominant microhabitat environmental factor influencing assemblages was flow velocity, which was correlated with percent deposited sediment conditions (primarily coarse sand) across 264 samples. Those findings suggested that macroinvertebrates may show sensitivity within specific habitat types defined by flow velocity. In this study, we investigated benthic macroinvertebrates within fast- and slow-water habitat types and determined their relationships with streambed sedimentation within those habitats. Habitats occurred in different overall total proportions

(n = 199, fast-water; 63%, slow-water; 37%) and showed significant differences in mean flow velocity, water depth, and percent deposited sediment. Total numbers of individuals and taxa richness were significantly higher in fast-water samples, while beta diversity was similar between habitats. Non-metric multidimensional scaling ordination of macroinvertebrates showed an assemblage gradient due to habitat type, with most taxa associated with fast-water habitats. Taxa with strongest correlations with the gradient produced by ordination included Ephemerella sp., Diplectrona sp., and Leuctra sp. for fast-water and

Ephemera sp. for slow-water habitats. Significant macroinvertebrate response to sedimentation (Spearman r > 0.70, P < 0.05) occurred almost exclusively in fast-water habitats, with only two taxa showing a significant response in slow-water habitats. Five of the ten most dominant taxa among the eight streams,

Ephemerella sp., Leuctra sp., Paraleptophlebia sp., Diplectrona modesta, and Tallaperla sp. showed significant response to sedimentation in fast-water habitats, no response in slow-water habitats, and smaller correlation coefficients with habitat data combined. Of the sensitive taxa, only Promoresia sp. and

Isoperla sp. had higher correlation coefficients resulting from habitat data combined. In most cases, combining habitat data typically reduced the strength of significant correlations. Indicator species analysis of the responsive taxa from fast-water habitats, using dominant substrate as the grouping factor, showed that significant indicators were mainly attributed to detritus and bedrock habitats. These findings suggest that a systematic stratified design focusing only on taxa associated with fast-water habitats may be an effective sampling strategy for investigations of sedimentation impacts in these headwater systems.

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Introduction

Benthic macroinvertebrates are exposed to a wide variety of hydrologic and physical conditions in mountain streams (Beschta and Platts 1986) where physical heterogeneity provides the habitat template for assemblage function (Hynes 1970, Hildrew and Townsend 1997, Wallace and Webster 1999). However, biological variability, especially in headwater streams (Heino et al., 2004), creates difficulty in establishing effective macroinvertebrate sampling designs that seek to determine stressor-response relationships and in developing effective biocriteria. Accordingly, debate continues regarding the most effective approach for sampling macroinvertebrates among their habitat, and numerous methods are used for stream biomonitoring (Rosenberg and Resh 1993).

Different benthic habitat types in streams often contain many different types of benthic organisms and exhibit varying assemblage structures (Rabeni 2000, Rabeni et al. 2002, Huryn and Wallace 1987).

The type of organisms present within different habitats and their associated ecological traits lead to a wide range of biota-environment relationships, thus creating diverse assemblages which likely respond differently to human perturbation. For sampling benthic macroinvertebrates during biological monitoring, habitat stratification is often used to limit that biological variability and to isolate factors other than physical habitat that are impacting biota (e.g., water quality, Barbour et al. 1999).

Sedimentation in headwater streams is a growing concern in the southeastern U.S., where it often is the primary focus of most watershed management programs. Previous studies have indicated that attributing sedimentation impacts to biota in mountain headwater streams is difficult due to the unknown dynamics of coarse sands along streambeds, the ubiquitous distribution of dominant taxa in the presence of deposited sediments, and the lack or weaknesses of relationships between deposited sediment levels and macroinvertebrate assemblages (Chapters 1 and 3).

Using a large number of microhabitat samples collected in headwater streams in the Chattooga

River watershed, we previously determined that flow was the predominant environmental factor shaping macroinvertebrate assemblages (Chapter 3). Therefore, investigations should be conducted to improve our understanding of sedimentation impacts within flow-mediated habitat types in headwater systems. One approach for benthic macroinvertebrate habitat classification associated with flow velocity conditions was

112

proposed and detailed by Hawkins et al. (1993). Their design consists of a three-tiered, hierarchical classification with the most general level being fast- or slow-water. That classification may be effective for precisely characterizing streambed habitats in physically heterogeneous mountain streams due to the predominance of variable flow velocities in those systems. Furthermore, because sedimentation is a component of otherwise natural substrate conditions, habitat classification using flow velocity eliminates having to stratify streambeds in the presence of pollutants that can potentially alter habitats (e.g., sedimentation). Therefore, investigations of sedimentation in highly heterogeneous mountain streams may benefit from a habitat classification based on flow velocity (i.e., Hawkins et al. 1993).

In this study, we used a broad habitat classification of fast- and slow-water habitats to determine if assemblages were distinct within those habitats and to investigate macroinvertebrate responses to sedimentation. Our goal was to compare biological information from the two habitats with a whole-reach sedimentation response (habitat data combined). Separating biological responses associated at those spatial scales will help to document areas of streams that provide the most information for monitoring sedimentation.

Methods

Study area

The Chattooga River watershed lies at the southern end of the Appalachian Blue Ridge mountains

(Fig. 4-1). Approximately 70% of the watershed is in public ownership as National Forest. Mountain streams in the region are generally coldwater, high-gradient tributaries and catchment divides reach elevations of 1500 m. Steep landscapes contain a variety of vegetation including pines (Pinus virginiana and P. echinata) and oaks (Quercus prinus and Q. coccinea), with higher elevation streams canopied by white pine (Pinus strobus) and eastern hemlock (Tsuga canadensis), and riparian areas at those higher elevations often containing dense understories of Rhododendron sp. Streams in the Chattooga River watershed are influenced by gneiss and shist rocks contained in loamy soils that are highly erodible and are primarily underlain by sedimentary and metamorphic bedrock (Van Lear 1995).

113

one watershed contained a road or portion thereof. Sites were selected to minimize the effects of contemporary watershed activities related to road-use and to isolate the mechanisms associated with the in- stream, micro-habitat scale effects of substrate environments on benthic macroinvertebrates. A detailed description of stream network characteristics in the Chattooga River watershed can be found in Hansen

(2001).

Field and laboratory methods

Eight streams were chosen for study in April 2003, with 6 located in Georgia, one in South

Carolina, and one in North Carolina. Streams were selected to ensure that sites were distributed across the

Chattooga River watershed and represented typical headwater mountain streams found in the region. We used a systematic longitudinal, sampling design at each site in order to cover the range of streambed habitats and associated deposited sediment levels. The systematic, longitudinal sampling design is a multihabitat design in that random sampling locations were selected along a longitudinal transect running parallel with the stream channel (Fig 3-2) (Lazorchak et al. 1988). Samples were taken from a 100-m reach within each site. To randomize the beginning point (i.e., downstream end) of each reach, a random distance (range 0-100 m) was stepped upstream, with the designated reach beginning at the end of that distance. Within each 100-m reach, macroinvertebrates were systematically sampled at 33 locations spaced 3 m apart on the longitudinal transect. At each sampling location, a measuring tape was extended across the stream channel perpendicular to the longitudinal transect and a random number was chosen to locate the sample a specified distance in 0.1 m increments from the left bank. A D-frame dip net (500-µm mesh) was used to sample benthic macroinvertebrates at the specified locations. The net was held in one position on the stream bottom, and the streambed was agitated by hand directly upstream of the net to dislodge the organisms. The area sampled was visually estimated to be a square with sides equal to the width of the dip net (area ≈ 0.09m2). Because of the heterogeneous nature of the streambed and the systematic and randomized sampling locations, a variety of substrate and current conditions were encountered. Therefore, the amount of time necessary to dislodge the organisms varied, but it generally ranged from 5 to 15 sec. Each macroinvertebrate sample was placed in a bag with 95% ethanol. In the laboratory, all macroinvertebrates were identified to the lowest possible taxonomic level, which was usually genus, using standard taxonomic references applicable to the region (Brigham and Brigham 1982,

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Merrit and Cummins 1996, Wiggins 1996). The numbers of organisms in each taxon were counted and the data were stored in Excel spreadsheets until later analyses.

From macroinvertebrate data, we developed habitat-specific and whole-reach metrics to relate to corresponding proportional sedimentation variables (discussed below). We chose our final metrics from a suite of thirty initial metrics considered to be good for detecting anthropogenic disturbance (Barbour et al.

1999), and we were specifically interested in those metrics that we thought should respond to changes in substrate conditions due to increasing sedimentation. Metrics belonged to one of five categories: richness, community balance, community composition, functional feeding group, habitat, and tolerance. To reduce the number of test metrics and redundancy associated with similar metrics, Pearson correlation coefficients were computed between metrics within all five categories. If two metrics were significantly correlated (P <

0.05, r > 0.70), only one was retained. Our goal was to remove redundant metrics while retaining at least two metrics per ecological category. As a result, 17 metrics were retained for further statistical analyses.

At macroinvertebrate sampling locations, the corresponding habitat was recorded as fast-water or slow-water based on the hierarchical stream habitat classification method of Hawkins et al. (1993). Within that classification scheme, geomorphic units are described as relatively homogenous areas of depth and flow, with sharp gradients in those conditions indicative of different streambed habitat. We chose not to set quantitative flow or depth criteria for habitat classifications in the field because we wanted to determine the accuracy of our visual assessments with a posteriori analyses of flow and depth per habitat. In doing so, statistically significant differences between the two would suggest the applicability of visual habitat delineations in the field.

At each transect, percent deposited sediment was estimated to the nearest 5% by visually examining the streambed substrate content within a 0.5m2 metal frame placed upon the streambed (range =

0 – 100%). A value of 1% was recorded in the case where deposited sediment was less than 5% but greater than 0% (Zweig and Rabeni 2001). We defined percent deposited sediment as substrate sizes < 2 mm (i.e., sand, silt, clay). One value of percent deposited sediment was recorded at each macroinvertebrate sampling location. Dominant and subdominant substrate were recorded within the metal frame and assigned one of the following numbers: bedrock = 9, boulder = 8, cobble = 7, large gravel = 6, small gravel = 5, sand = 4, silt = 3, clay = 2, and 1 = detritus. Dominant substrate was the substrate type occurring over the majority of

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the metal frame area, with subdominant substrate be the second substrate type with the most streambed area covered within the frame. Flow velocity (Marsh – McBirney® flow meter) and water depth were recorded from just within the front edge of the metal frame at each sampling location.

We used the variable dominant substrate to develop proportional, reach-scale streambed substrate variables corresponding to habitats and the entire reach. For example, 33 random observations of dominant substrate per stream allowed us to calculate the variable percent of transects with sand as the dominant substrate (i.e., T sand, Table 4-4). In addition, three variables were developed that corresponded to the entire reach but were not intended to be habitat specific during data analysis, percent embeddedness, percent fines less than 2mm, and D50. Percent fines less than 2mm and D50 (particle size where the cumulative percent of half of the sample is finer than the given size, D) were developed from Wolman pebbles counts (Wolman 1954). Pebble counts (n = 100) were conducted in fast flowing areas typically comprised of cobble gravel substrate. The 100 pebble measurements were distributed over the fast flowing areas within the reach and generally involved pebble measurements at three to four locations along the reach. Percent embeddedness was determined in pool tail-outs, which are the areas where the downstream section of the pool approaches the pool tail crest and was visually estimated to be the downstream, lower

25% of a pool. There, cobble substrate that does not reach the downstream riffle may accumulate and become embedded by transported fine sediments, thus providing a measure of deposited sediment for a stream reach. Percent embeddedness was calculated by dividing the embedded height of the cobble, indicated by a silt or sediment line, by the total height of the cobble along the axis perpendicular to that line.

Data analysis

We removed univariate flow and depth outliers to focus our analysis on the influence of deposited sediments. Outliers were found by relativizing flow and depth data (to standard deviates) and then removing those samples that contained high absolute values (> 1.0) for at least one variable (McCune and

Grace 2002). That criterion resulted in the removal of 65 transect samples from the original 264 subsample count, with 199 habitat-specific samples remaining in the dataset.

We first conducted an analysis to determine if habitat-specific traits varied across streams (2 habitats among eight streams) using graphical observations and ANOVA. We then investigated biological

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differences among habitats per stream through analyses of species richness, total number of species, and beta diversity. A non-metric multidimensional scaling ordination procedure (NMS; Kruskal 1964, McCune and Grace 2002) was used to evaluate graphically display macroinvertebrate assemblage relationships among habitats and streams. We computed NMS with the autopilot mode using Sorenson distances on 40 runs of log(X+1) macroinvertebrate abundances in PC-ORD (Glenedon Beach, OR, McCune and Mefford

1999). Abundances were log transformed to reduce the influence of extremely large values. Taxa with fewer than 20 occurrences (10% of samples) were removed prior to analysis to reduce noise in the dataset, specifically due to many zero values (Gauch 1980). A Monte Carlo test of significance on randomized data using 50 runs was computed with the NMS computations to validate strength of the ordination (McCune and Grace 2002).

Following the analyses of physical and biological habitat comparisons across streams, habitat data were pooled to yield one dataset per habitat and whole reach across streams (fast water; n = 127, slow water; n = 72, whole reach; n = 199). After transforming environmental variables according to data distribution, ANOVA was used to analyze differences in mean flow, depth, width, and deposited sediment across habitats. Equal variance and normality were checked due to the high numbers of samples and unequal sample size. However, removing flow and depth outliers and data transformation alleviated normality problems.

Using the pooled habitat and whole reach macroinvertebrate abundance data, Pearson correlations were computed on relative abundances per habitat- and whole-reach specific percent deposited sediment levels. Shown graphically, those correlations would have appeared as the relative abundance distributions in 3.6 (Chapter 3). We chose to conservatively indicate macroinvertebrate sensitivity to sedimentation as those taxa having a correlation coefficient > .70. In addition, indicator species analysis (Dufrêne and

Legendre 1997, McCune and Grace 2002) was conducted to determine if sensitive taxa differentiated between dominant substrate types within a particular habitat or with habitat data pooled. Indicator species analysis computes a final indicator value (IV) and P-value, to determine if any taxa pointed to a dominant substrate without error (i.e., IV = 100). P-values are computed from Monte-Carlo data randomizations, where the P-value indicates the proportion of time the randomized IV equals or exceeds the actual IV calculated from the original data.

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Following correlations of individual macroinvertebrate relative abundances across percent deposited sediment levels, we investigated the relationships of our 17 assemblage metrics with the developed, reach-scale proportional sedimentation variables described in Table 4-4.

Results

Habitat differences per stream

In-field habitat classification based on visual assessment proved effective in that both flow and depth among habitats varied consistently across streams (Fig. 4-2). We investigated biological differences between habitats both among streams (n = 8, Table 4-1), and with habitat-specific samples combined

(Table 4-2). Fast-water habitats were more frequent in our streams, and excluding Bailey Branch (n = 7), fast-water habitat frequencies per stream ranged from 15 to 22 while slow-water habitat frequencies ranged from 6 to 12 across streams (Table 4-1). Average taxa richness among streams ranged from 14.7 to 21.3 in fast-water habitats and from 8.3 to 18.8 in slow-water habitats. Beta diversity was similar for the two habitats (mean beta diversity = 3.1 fast-water, 3.0 slow-water, n = 8). NMS ordination analysis using habitat-specific macroinvertebrate log(x+1) transformed abundances per stream showed an overall assemblage gradient, with most taxa associated with fast-water habitats (Fig 4-3). NMS autopilot chose a

2-dimensional representation, with statistically significant reductions in stress (final stress = 11.02) when compared to randomized data (Fig 4-4). Axes 1 and 2 explained 63% of the variance in macroinvertebrate abundance data. Taxa with the strongest correlations with the habitat gradient (i.e., positive axes 1 and 2) were Ephemerella sp., Diplectrona sp., and Leuctra sp. for fast-water, and Ephemera sp. for slow-water habitats (Fig. 4-3).

Habitat differences with data combined across streams

Following flow and depth outlier removal, our dataset was comprised of 127 fast-water and 72 slow-water samples. With habitat data combined across streams, significant differences were found for all environmental variables; flow velocity, depth, and % deposited sediment (all P < 0.001, Table 4-2).

Average dominant substrate was 6.52 (small-gravel to large-gravel) for the fast-water and 5.11 (primarily small gravel) for the slow-water habitats (Table 4-2). The overall proportions of dominant substrate per

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habitat are shown graphically (Fig. 4-5). Mean deposited sediment among fast- and slow-water habitats was 19% and 60%, respectively. Nearly three times more individuals were collected from fast-water than slow-water habitats (10, 357 vs. 3,789), with a total of 98 taxa collected from fast-water habitats and 84 collected from slow-water habitats (Table 4-2).

Macroinvertebrate relationships with deposited sediments

A total of 25 taxa were found to be sensitive to deposited sediments either from a particular habitat or in the whole reach (Table 4-3). Almost all taxa showing sensitivity were from fast-water habitats. No taxa had a correlation coefficient >.70 for the slow-water habitats. Addition of slow-water macroinvertebrate abundance data typically reduced the fast-water correlation coefficient. However,

Promoresia sp. and Isoperla sp. had higher correlation coefficients for the whole-reach data. Of the sensitive taxa in fast-water habitats, all were inclusive to one of the major aquatic orders: Ephemeroptera,

Plecoptera, Trichoptera, or Diptera. One member of the Odonata, Lanthus sp., had a significant relationships with deposited sediments in both habitats (P < 0.05), but showed different correlation coefficients among habitats and whole reach data (Table 4-3), with fast-water data producing the only coefficient >0.70. Indicator species analysis showed that deposited-sediment sensitive macroinvertebrates showed weak preference for dominant substrate. Only six taxa had significant indicator species values.

Tallaperla sp., Amphinemura sp., and Isoperla sp. all were shown to be significant indicator species associated with the organic (i.e., detritus/organic, n = 10 among 264 observations) dominant substrate type, while Epeorus sp. and Promoresia sp. were significant indicators of bedrock substrate (n = 34).

Relationships of assemblage metrics and reach-scale sedimentation among habitats

Significant macroinvertebrate assemblage metrics and sedimentation relationships were more numerous in the fast-water habitats (all P < 0.05, Table 4-4). Three metrics related to slow-water habitats were significantly related to % embeddedness: Simpson’s diversity, percent 1 dominant, and percent tolerant organisms. Those findings associated with slow-water habitats suggest that overall community balance and diversity as described by metrics is affected when cobble in slow-water habitats become embedded, thus eliminating additional substrate availability (in addition to dominant sand substrate) in those habitats. Whole-reach data produced 10 significant correlations, with number of EPT taxa and % clingers showing a relationship with whole-reach deposited sediment. Ten assemblage metrics were

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significantly related to sedimentation variables in fast-water habitats, with five of those metrics associated with the variable percent fines < 2mm.

Discussion

Sampling among fast- and slow-water habitats produced a distinct macroinvertebrate assemblage gradient and provided information that will aid investigations of sedimentation problems in mountain streams. The habitat classification detailed by Hawkins et al. (1993) was an effective means of habitat stratification in Chattahoochee National Forest streams, especially in the presence of sedimentation- induced changes in streambed habitats. In a recent study, Effenberger et al. (2006) suggested that flow velocity and water depth are primary influential habitat parameters of benthic macroinvertebrate microdistribution when associated with concurrent streambed disturbance (i.e., flooding). Rabeni et al.

(2002) found community similarity (based on taxon relative abundances) significantly increased when using a three-level flow velocity habitat classification approach, and communities within habitats designated as fast-water were more spatially and temporally consistent when compared to slow-water communities. These findings suggest that habitat stratification using flow-velocity may provide effective biological information in the presence of changing streambed conditions due to sedimentation.

Slow-water habitats occurred less frequently than fast-water habitats and excluding them from stream monitoring may reduce field-sampling expenditures, while focusing on habitats that provide the most biological information. Zweig and Rabeni (2001) was one of the only sedimentation studies we found that investigated individual taxon response to percent deposited sediments. They developed the Deposited

Sediment Biotic Index (DSBI) for Ozark mountain warmwater streams using taxon density per deposited sediment class. However, the greater range of deposited sediments in their study streams led to some taxa preferring high levels of deposited sediments. We found no taxa with significant, positive correlations due to high levels of deposited sediments (Table 4-3).

The overall proportions of habitat frequencies (fast, 63%; slow 37%) were consistent with that of riffle and pool habitat proportional areas (riffle, 58%; pool, 23%) determined by Huryn and Wallace (1987) in a study of habitat-specific production in a Southern Appalachian mountain stream. Our delineations were based on disjunct, point locations (n = 33 per stream) while theirs were measurements of habitat

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cover. Further studies may confirm that point measurements of habitats may act as surrogates for reach- scale habitat coverage, because both are habitat proportions. However, the transect interval along reaches

(3 m in our study) may need to be spaced appropriately as to encounter sequential habitats or multiple locations within large habitat areas. Physical differences associated with flow and depth among habitats varied consistently across streams and those findings supported our visual classification of habitats in the field. Therefore, replication of habitat stratification for field sampling and stream monitoring could easily be performed with initial quantitative checks on flow and depth per sampling session. Peterson and Rabeni

(2001) found that current velocity and depth accounted for 81% of the variance in channel units and suggested using those variables for differentiating habitats in warmwater streams. Our reported flow and depth ranges may guide habitat stratification associated with biomonitoring in Chattahoochee National

Forest headwater streams (Table 4-2).

We found distinct differences in macroinvertebrate abundance between the two habitat types, yet many of the dominant taxa were found in both habitats. Halwas et al. (2005) found similar results from high gradient streams on Vancouver Island, British Columbia, in that macroinvertebrate abundance was highest in riffles but overall assemblage composition among riffle and pool habitats were not distinct.

Rabeni et al. (2002) found differences in taxa between fast- and slow-water habitats, and finer habitat classification within those flow groups (e.g., edgewaters, high and low gradient riffles) produced greater assemblage differences. Their study was in a low-gradient, Ozark Mountain warm-water stream that may have more distinct (occupying larger streambed areas) habitat types when compared to high-gradient, Blue

Ridge streams. However, studies should be conducted to assess sedimentation impacts in our streams at the smallest habitat classification possible. Determining that level of stressor-response relationship may provide the most effective biological information in terms of organism predictability within habitats and their response to sedimentation.

Systematic sampling along streambeds provides generalized, reach-scale habitat proportions and the relative occurrence of habitat types along headwater stream reaches. Those habitat proportions are important for understanding habitat templates along reaches and the macroinvertebrate function provided by those benthic habitats. Hoover et al. (2006) found that streambed morphology and substrate characteristics (often attributed to different habitat types) creates a partitioning of detrital resources

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between riffle and pool habitats. An important implication from their study was that subtrate geometry affects leaf retention. When associated with sedimentation, subtrate geometry (specifically the overall substrate area) may become modified due to increasing deposited sediment levels around substrates, thus potentially altering coarse particulate organic matter (CPOM) retention along streambeds. Furthermore, increasing deposited sediment levels around individual substrate may alter flow patterns, thus further compromising the leaf-retention properties of sediment-impacted substrates.

The larger numbers of taxa recorded from fast-water habitat is influenced by the higher number of fast water habitats and the greater substrate diversity and wider range of environmental conditions within those habitats. Although the number of taxa was considerably larger in the fast-water habitats, beta diversity was similar among habitats, suggesting that the average number of species per fast- or slow-water sub-sample is related to the total number of species within the habitat. Additionally, beta diversity values suggest that each fast- or slow-water sub-sample across streams may yield approximately 1/3 of the total slow-water taxa for the similar streams in the region (Table 4-1).

Several studies have investigated the effects of deposited sediments on benthic macroinvertebrates both in natural settings (Lenat 1994, Zweig and Rabeni 2001) and experimentally (Angradi 1999,

McClelland and Brusven 1980). Most of those studies determined assemblage changes using macroinvertebrate metrics and found a decrease in the generally-sensitive EPT taxa due to the decrease in interstitial spaces. Lenat (1994) found a decrease in density but found no distinct shift in the overall assemblage composition. We found a number of assemblage metrics to be significantly correlated to sedimentation variables and most of those relationships were from fast-water habitats. A key implication of the metric-sedimentation correlations is that sedimentation variables developed from habitat-specific substrate measurements (i.e., % cobble embeddedness in ‘pool’ tail-outs and % fines < 2mm from ‘riffle’ pebble-counts) were often related to a habitat specific metric.

Stratifying sampling to focus on fast-water habitats acquires organisms that react more predictably to sedimentation. In our study, we found the greatest amount of information related to macroinvertebrate response to sedimentation came from the individual taxon correlations with percent deposited sediment.

From those deposited-sediment sensitive taxa, we can develop biological endpoints using a bottom-up approach rather than relying on traditional metrics used to infer other forms of pollution impact. In

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addition, focusing on a particular habitat will facilitate further investigations of sedimentation impact on macroinvertebrates in mountain streams and will strengthen the biomonitoring framework in the Chattooga

River watershed.

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Acknowledgements

We thank Craig Roghair and Dan Nuckols of the USFS Center for Aquatic Technology Transfer and Jamie

Roberts for assistance in data collection. We are grateful to Stephen Hiner, Amanda Ellis, Trisha Voshell,

Rachel Wade, and Kathy Hanna for laboratory assistance. Thanks to Charlene Breeden of the USFS

Tallulah Ranger District in Georgia for providing resources in the Chattahoochee National Forest.

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Table 4-1 Benthic macroinvertebrate diversity among the two habitat types along 100 m reaches in eight streams in the Chattooga River watershed. Beta diversity is the total number of species divided by the average number of species. Fast-water and slow-water individual row values are the total number of fast and slow water samples combined across streams following outlier removal (see methods).

average total

species number habitat richness beta of Flow habitat / stream sample size (± s.d.) diversity species fast-water 127 18.2 (5.7) 5.4 98 Bailey Branch 7 14.7 (7.5) 2.9 42 Goldmine 18 20.9 (5.8 2.8 59 Holcomb tributary. 18 17.3 (5.1) 3.0 53 Harden Branch 15 18.5 (5.7) 3.0 57 Lick Log 16 21.3 (6.2) 2.8 60

Martin Creek 15 17.0 (5.4) 3.2 55 Overflow tributary 16 16.3 (5.5) 3.4 55 Slatton Branch 22 17.4 (4.3) 4.0 71

slow-water 72 14.7 (5.9) 5.7 84 Bailey Branch 12 15.4 (6.3) 3.9 60

Goldmine 11 17.2 (4.0) 2.7 47 Holcomb tributary 8 16.6 (3.2) 2.5 41 Harden Branch 10 11.7 (4.5) 3.2 38

Lick Log 10 18.8 (5.1) 2.3 43 Martin Creek 8 8.3 (4.7) 3.5 29 Overflow tributary 7 12.4 (6.5) 3.1 39 Slatton Branch 6 15.3 (7.2) 2.6 40

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Table 4-2 Physical and biological characteristics of fast- and slow-water habitats in eight Chattooga River headwater streams. Mean ±1 s.d. (range).

fast-water slow-water physical nA 127 72 mean flow* 0.28 ±0.15 (0.03 - 0.62 m/sec) 0.11±0.08 (0 - 0.34 m/sec) mean depth* 9.39 ±3.67 (3.0 - 17.0 cm) 15.34 ±7.00 (5 - 30 cm) mean width 3.14 ±0.98 3.06 ±0.90 mean deposited sediment* 0.19 ±0.22 0.60 ±0.31 average dominant substrate 6.52 ±1.62 5.11 ±0.91 biological total # individuals 10,357 3,789 total # taxa 98 84 mean taxon richness 17.41 ±6.05 (5 - 31) 13.43 ±6.22 (2 - 24) ATotal number of samples per habitat, excluding outliers (see methods). *significant for between-habitat comparisons (ANOVA P <0.01)

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Table 4-3 Spearman correlations of macroinvertebrate abundances and percent deposited sediment associated with fast-water, slow-water, and whole reach data. Only significant relationships with a correlation coefficient > 0.70 for any data type (fast, slow, or whole reach) are shown. Largest correlation coefficients per taxon are in bold font.

taxon fast-water slow-water whole-reach Amphinemura sp. -0.742 - -0.720 Baetis (complex) -0.724 - -0.535 Dicranota sp. -0.872 - -0.793 Diplectrona modesta -0.752 - -0.704

Dolophilodes sp. -0.767 - -0.647 Epeorus sp. -0.739 - -0.626 Ephemerella sp. -0.901 - -0.788 Glossosoma sp. -0.814 - -0.728 Isoperla sp. -0.697 -0.501 -0.704 Lanthus sp. -0.723 0.466 -0.446 Leuctra sp. -0.726 - -0.618 Paraleptophlebia sp. -0.767 - -0.759 Polycentropus sp. -0.879 - -0.739

Promoresia sp. -0.667 -0.449 -0.701 Prosimulium sp. -0.800 - -0.687 Pteronarcys sp. -0.713 - -0.693 Remenus sp. -0.750 - -0.714 Rhyacophila sp. -0.695 - -0.711 Simulium sp. -0.704 - -0.661 Stenonema sp. -0.793 - -0.725 Tallaperla sp. -0.809 - -0.773 Tanytarsini sp. -0.789 - -

Tipula sp. -0.722 - -

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Table 4-4 Spearman correlations of habitat and whole reach-specific metrics and reach-scale sedimentation variables. All significant relationships are shown (P < 0.05). Sedimentation variables represented by lower-case letters are habitat specific but for the entire reach, upper cases are habitat specific, transect means. Dash indicates no metric x sediment correlations were significant. A = whole reach deposited sediment, B = percent transects with > 50% deposited sediment, C = percent of transects with fines as dominant substrate, D = mean fast water percent deposited sediment, E = mean slow water percent deposited sediment, a = percent embeddedness, b = percent fines less than 2mm, and c = D50.

metric fast-water slow-water whole-reach Evenness c - C

Simpsons Diversity b a a,b No. Taxa b - a No. EPT Taxa b - A,B

percent 1 Dominant b a a percent Chironomidae D,B - B percent Plecoptera C - -

percent Burrowers b - - percent Clingers - - A,c percent Tolerant Organisms - a -

No. Intolerant Taxa D - -

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Fig. 4-1 Map of the southeastern U.S. showing the location of the Chattooga River watershed and the smaller subwatersheds containing our study sites. See page 92 for stream names and abbreviations.

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Fig. 4-2 Mean (± s.d.) for (A) flow velocity and (B) water depth per habitat among 100m reaches in eight

Chattooga River headwater streams.

0.8

A. 0.7 Fast water Slow water 0.6

0.5

0.4

0.3

flow velocity (m/s)

0.2

0.1

0.0

BA GM HC HR LL MA OF SL

B. 30 Fast water Slow water

water depth (cm) depth water 10

0 BA GM HC HR LL MA OF SL

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Fig. 4-3 NMS ordination of 16 habitat-specific (fast- and slow-water) samples from eight Chattooga River headwater streams. Species were included in the second matrix for overlay on the ordination graph.

Radiating lines indicate the relative direction and strength of species with the ordination pattern.

GM F LL F

Ephemere Stenonem SL F Tipula Baetis Lanthus Oulimniu Diplectr Dolophil HR F Acroneur OF F OLIGOCHA Leuctra MA F GM S Paralept LL S HC F Remenus Dicranot Tallaper

Amphinem BA S Ephemera BA F

2 Axis

HC S HR S SL S

OF S

MA S

Axis 1

Fig. 4-4 In reference to figure 4-3 above, NMS scree plot comparing the best run using real data with randomized runs. The line indicates reduction in stress as the fit of the data (dimensions on x-axis) increases. NMS autopilot mode chose a 2-dimensional solution (P = 0.012, final stress = 11.02).

60 Real Data Randomized Data Maximum

Mean Minimum 40

Stress

135 Dimensions

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Fig. 4-5 Dominant substrate occurrence among habitat samples combined across eight Chattooga River headwater streams.

n = 127 n = 72

100%

90% 80% bedrock 70% boulder cobble 60% large gravel 50% small gravel 40% sand 30% detritus

20%

10%

habitat proportion per cumulative 0% fast-water slow-water

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CHAPTER 5

Development of a sedimentation biotic index for macroinvertebrates in small forested streams in the

Chattahoochee National Forest

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Abstract

Freshwater biomonitoring programs often use benthic macroinvertebrates as indicators of environmental quality in streams. One common approach is to determine individual macroinvertebrate tolerance to a specific pollutant or stream condition and to summarize individual tolerances into an index value that describes the stream “health.” Degraded physical stream conditions in the Chattahoochee

National Forest due to sedimentation have prompted the development of effective procedures for monitoring sedimentation. In this study, we developed a sedimentation biotic index using two different methods and determined relationships with deposited sediment conditions at both the sample and reach scale. The index produced from cumulative abundances of the 30 dominant taxa from fast water habitats showed a moderate correlation (R2 = .35) with percent deposited sediment levels below 30%, yet was weakly related to the entire percent deposited range due to all 30 taxa reaching 50% cumulative abundances within 15% deposited sediment. The sedimentation biotic index produced from indicator species analysis showed no relationships with percent deposited sediment among individual samples, probably due to high variability among the 264 samples. We developed habitat-specific and habitat-pooled indices at the reach scale and found a stronger relationship with percent embeddedness with indices developed from fast-water and habitat-pooled data when compared to the index developed from slow-water data. Of all measures of sedimentation at the reach scale, percent embeddedness was the variable that was significantly related to reach-scale sedimentation biotic indices.

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Introduction

Freshwater macroinvertebrates are a diverse group of organisms that occupy all types of streambed habitats. Their ability to inhabit most types of benthic habitats sustains macroinvertebrate densities and assemblage diversity, thus providing an effective and critical link in the transport of energy through aquatic systems (Wallace and Webster 1996). Because of their significant ecological importance, changes induced by anthropogenic disturbances can have adverse effects on natural stream function

(Waters 1995). In the presence of pollution, those changes are a result of individual macroinvertebrate response to perturbation, with some species sensitive and others insensitive to a particular system stressor.

Because of their high numbers, the variety of available streambed habitats, and differential response to environmental stressors, benthic macroinvertebrates are valued as effective indicators of water quality

(Barbour et al. 1999).

High levels of deposited sediments upon streambeds can be detrimental to aquatic biota (Waters

1995) and have been shown to compromise normal benthic macroinvertebrate assemblage structure (Lenat

1994). Macroinvertebrate sensitivity to deposited sediments has typically been assessed using macroinvertebrate assemblage metrics or multimetric indices (e.g., Lenat 1994, Rabeni et al. 2005), which provide an assemblage summary based on enumerations, taxa richness, functional and habitat groups, and biotic indices (Rosenberg and Resh 1993). However, most studies report at most subtle effects due to deposited sediments using metric summaries. Although several studies have investigated changes to macroinvertebrate assemblages due to spate (i.e., high intensity and short duration) inputs of anthropogenically-derived fine sediment to stream channels, none have investigated individual macroinvertebrate response to deposited sediments due to non-spate inputs to headwater streams.

Besides using assemblage metrics for evaluating assemblage health, an alternative approach for determining the macroinvertebrate sensitivity to sedimentation is to determine individual tolerance values.

Macroinvertebrate assemblage health can then be determined by summarizing individual tolerance scores into an overall biotic index. In the past, biological indices using macroinvertebrates have been effective in assessing organic pollution through recording water chemistry parameters and looking at macroinvertebrate abundance and frequency across a water quality gradient (Chutter 1972 , Hilsenhoff 1987, Lenat 1993).

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More recently, Zweig and Rabeni (2001) developed sedimentation tolerance scores and a deposited sediment biotic index (DSBI) for macroinvertebrates from glide habitats in Missouri Ozark streams.

In the Chattooga River watershed, sedimentation is a non-point source stressor that has received much attention in the past decade (Van Lear 1995, USEPA 1999). Previous studies using historical data suggested that benthic macroinvertebrate metric response to sedimentation among low, medium, and high sedimentation categories are subtle at the reach-scale (Chapter 1), and macroinvertebrate response to deposited sediment conditions are different between flow-mediated habitat types (i.e., fast- and slow water habitats, Chapter 4). Those findings provide the impetus for developing individual tolerance values to deposited sediment conditions. In this study, we assigned deposited sediment tolerance values using two different methods and summarized tolerance values into an overall sedimentation biotic index. Because of the differential response to deposited sediment conditions between fast water and slow water habitat types, we developed and tested habitat-specific biotic indexes against several substrate variables at the sample, habitat, and reach scale.

Methods

Study area

The Chattooga River watershed lies at the southern end of the Appalachian Blue Ridge mountains

(Fig. 5-1). Approximately 70% of the watershed is in public ownership as National Forest. Mountain streams in the region are generally coldwater, high-gradient tributaries and catchment divides reach elevations of 1500 m. Steep landscapes contain a variety of vegetation including pines (Pinus virginiana and P. echinata) and oaks (Quercus prinus and Q. coccinea)., with higher elevation streams canopied by white pine (Pinus strobus) and eastern hemlock (Tsuga canadensis), and riparian areas at those higher elevations often containing dense understories of Rhododendron sp. Streams in the Chattooga River watershed are influenced by gneiss and shist rocks contained in loamy soils that are highly erodible and are primarily underlain by sedimentary and metamorphic bedrock (Van Lear 1995).

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contemporary watershed activities related to road-use and to isolate the mechanisms associated with the in- stream, micro-habitat scale effects of substrate environments on benthic macroinvertebrates. A detailed description of stream network characteristics in the Chattooga River watershed can be found in Hansen

(2001).

Field and laboratory methods

Eight streams were chosen for study in April 2003, with 6 located in Georgia, one in South

Carolina, and one in North Carolina. Streams were selected to ensure that sites were well distributed across the Chattooga River watershed and represented typical headwater mountain streams found in the region.

We used a systematic longitudinal, sampling in order to cover the range of streambed habitats and associated deposited sediment levels. The systematic, longitudinal sampling design is essentially a multihabitat design in that random sampling locations were selected along a longitudinal transect running parallel with the stream channel (Fig 3-2) (Lazorchak et al. 1988). Samples were taken from a 100-m reach within each site. To randomize the beginning point (i.e., downstream end) of each reach, a random distance (range 0-100 m) was stepped upstream, with the designated reach beginning at the end of that distance. Within each 100-m reach, macroinvertebrates were systematically sampled at 33 locations spaced 3 m apart on the longitudinal transect. At each sampling location, a measuring tape was extended across the stream channel perpendicular to the longitudinal transect and a random number was chosen to locate the sample a specified distance in 0.1m increments from the left bank. A D-frame dip net (s500-µm mesh) was used to sample benthic macroinvertebrates at the specified locations. The net was held in one position on the stream bottom, and the streambed was agitated by hand directly upstream of the net to dislodge the organisms. The area sampled was visually estimated to be a square with sides equal to the width of the dip net (area ≈ 0.09m2). Because of the heterogeneous nature of the streambed and the systematic and randomized sampling locations, a variety of substrate and current conditions were encountered. Therefore, the amount of time necessary to dislodge the organisms varied, but it generally ranged from 5 to 15 sec. Each macroinvertebrate sample was placed in a bag with 95% ethanol. In the laboratory, all macroinvertebrates were identified to the lowest possible taxonomic level, which was usually genus, using standard taxonomic references applicable to the region (Brigham and Brigham 1982,

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Merrit and Cummins 1996, Wiggins 1996). The numbers of organisms in each taxon were counted and the data were stored in Excel spreadsheets until later analyses.

At macroinvertebrate sampling locations, the corresponding habitat was recorded as fast-water or slow-water based on the hierarchical stream habitat classification method of Hawkins et al. (1993). Within that classification scheme, geomorphic units are described as relatively homogenous areas of depth and flow, with sharp gradients in those conditions indicative of different streambed habitat. At each transect, percent deposited sediment was estimated to the nearest 5% by visually examining the streambed substrate content within a 0.5m2 metal frame placed upon the streambed (range = 0 – 100%). A value of 1% was recorded in the case where deposited sediment was less than 5% but greater than 0%. We defined percent deposited sediment as substrate sizes < 2mm (i.e., sand, silt, clay). One value of percent deposited sediment was recorded at each macroinvertebrate sampling location. Dominant and subdominant substrate were recorded within the metal frame and assigned one of the following numbers: bedrock = 9, boulder =

8, cobble = 7, large gravel = 6, small gravel = 5, sand = 4, silt = 3, clay = 2, and 1 = detritus. Dominant substrate was the substrate type occurring over the majority of the metal frame area, with subdominant substrate be the second substrate type with the most streambed area covered within the frame. Flow velocity (Marsh – McBirney® flow meter) and water depth were recorded from just within the front edge of the metal frame at each sampling location.

Pebble counts (n = 100) were conducted in fast flowing areas typically comprised of cobble gravel substrate. The 100 pebble measurements were distributed over the fast flowing areas within the reach and generally involved pebble measurements at three to four locations along the reach. Percent embeddedness was determined in pool tail-outs, which are the areas where the downstream section of the pool approaches

141

the pool tail crest and was visually estimated to be the downstream, lower 25% of a pool. There, cobble substrate that does not reach the downstream riffle may accumulate and become embedded by transported fine sediments, thus providing a measure of deposited sediment for a stream reach. Percent embeddedness was calculated by dividing the embedded height of the cobble, indicated by a silt or sediment line, by the total height of the cobble along the axis perpendicular to that line.

Development of the sedimentation biotic index

Two methods were used to develop macroinvertebrate tolerance values for the overall biotic index: (1) indicator species analysis, and (2) 50% taxon cumulative abundances across percent deposited sediment categories. Indicator species analysis computes a final indicator value (IV) and P-value, to determine if any taxa pointed to a dominant substrate without error (i.e., IV = 100). P-values are computed from Monte-Carlo data randomizations, where the P-value indicates the proportion of time the randomized

IV equals or exceeds the actual IV calculated from the original data (McCune and Grace 2002). For determining sedimentation categories for indicator species analysis, we used a group optimization procedure where sedimentation categories (percent deposited sediment intervals) were adjusted until the maximum number of indicator species were observed across a particular percent deposited sediment grouping. Therefore, our first indicator species grouping factor was simply all recorded deposited sediment categories. From that initial grouping, we subjectively grouped sediment categories until the maximum number of indicator species across the adjusted sedimentation range was found. Following the establishment of the optimal percent deposited sediment grouping that produced the highest number of significant indicator species, tolerance values were assigned according to the level of percent deposited sediment they were associated with from the most optimal grouping (i.e., percent deposited sediment grouping that produced the largest number of indicator species).

The second method for developing macroinvertebrate tolerance values involved calculating taxon- specific, 50% cumulative abundances over increasing levels of percent deposited sediment for the thirty dominant taxa. The thirty dominant taxa were determined by combining abundances for all taxa across samples and streams for those taxa occurring in at least 15% of the samples and present in at least six of the eight streams. In addition, we chose to conservatively calculate tolerance values with this method by calculating cumulative abundances within fast-water habitats only and with flow and depth outliers

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removed within those habitats. That focuses the accumulation of taxon abundance within a defined habitat, while reducing the potential influence of flow and depth on abundance accumulation patterns in those habitats. Outliers were determined by relativizing flow and depth data (to standard deviates) and then removing those samples that contained high absolute values (> 1.0) for at least one variable (McCune and

Grace 2002). That criterion resulted in the removal of 40 fast-water transect samples, reducing the number of fast-water transect samples from 167 to 127. From the remaining fast-water transects, cumulative abundances per deposited sediment interval (.05) were calculated. Abundances were summed for each deposited sediment category, relativized, and combined to yield the percent of the taxa occurring in a specific deposited sediment interval (Zweig and Rabeni 2001). Taxa were then assigned tolerance values based on observed groupings of taxon 50% cumulative abundances among levels of percent deposited sediment, where relative, low levels of percent deposited sediment at which 50% cumulative abundance occurs suggests taxon intolerance to deposited sediments.

Following calculations of taxon-specific tolerance values using the 2 methods described above, we calculated the overall sedimentation biotic index using the equation:

∑(TViNi) SBI = (Eq. 5-1) Total N

where TVi is the tolerance of the ith taxa, Ni is the abundance of the ith taxa, and Total N is the number of individuals in the sample. The equation is based on a biological index first developed by Chutter (1972) and then later modified for other regions (Hilsenhoff 1977, Lenat 1993).

Data analysis

Relationships of the developed SBI with sedimentation were investigated at the individual transect scale using absolute values of percent deposited sediment and at the reach-scale using composited abundances per reach and habitat-specific abundances. Therefore, relationships of the SBI and sedimentation at the reach scale were investigated using proportional, reach-scale sedimentation measures, percent embeddedness, percent fines < 2mm, and D50. Relationships of the developed biotic index with percent deposited sediment were investigated at the individual transect scale (i.e., 264 SBIs x 264 percent

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deposited sediment estimates) and at the habitat-specific and habitat-pooled using composited abundances and proportional reach scale sedimentation measurements. Linear regressions were computed using Sigma

Stat version 3 (SPSS Inc., Chicago, IL).

Results

Derivation of tolerance values

Indicator species analysis among the categorized percent deposited sediment groupings produced from 15 to 30 significant indicator taxa (Table 5-1). The group with the largest number of significant indicators had a four-level categorization of percent deposited sediment. Therefore, that group was used to assign tolerance values to the 30 significant indicators within that group (Table 5-2). Of the 30 taxa, 9 were assigned the value 1 (intolerant), 5 were assigned 2 (moderately intolerant), 12 were assigned 3 (moderately tolerant), and four taxa were assigned 4 (tolerant). Those tolerance values were inserted into equation 5-1 to yield the SBI-IS.

Tolerance values derived from cumulative abundances for the thirty dominant taxa in fast-water habitats and are shown in Table 5-3. The 50% percent cumulative abundance for all taxa occurred at ≤10% deposited sediment. The number of taxa per deposited sediment category within, and including, that deposited sediment range was as follows: 9 taxa at 1%, 11 taxa at 5%, and 10 taxa at 10% deposited sediment. Those tolerance values were inserted into equation 5-1 to yield the SBI-CA.

SBI relationships with percent deposited sediment at the sample scale

Regression results showed that the SBI-IS was not related to percent deposited sediment at the sample scale. In addition, fast-water and slow-water specific SBI-IS at the sample scale showed no relationships with percent deposited sediment. However, the SBI-CA showed a statistically significant relationship with deposited sediment at the sample scale within a 0 – 30% deposited sediment range (R2 =

.35, P < .001, Fig 5-2).

Because the SBI-IS was not related to percent deposited sediment at the sample scale (n = 264), we further explored relationships with percent deposited sediment by removing moderately intolerant and moderately tolerant. Specifically, taxa with the following tolerance values were removed from the initial

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SBI-IS list: (1) TV = 2 , and (2) TV = 2 and TV = 3s with < 0.25 indicator value (the weakest 0.25 indicators in all TV = 3). In so doing, removing taxa with those indicator values increased the distance, in terms of 50% cumulative abundance over percent deposited sediment, between tolerant and intolerant taxa, placing more emphasis on taxa at opposite ends of the percent deposited sediment range. Modified SBI-IS values were then calculated for the macroinvertebrate datasets remaining after removing taxa with (1) and

(2) above. Henceforth, (1) will be referred to as SBI-IS-1 and (2) will be referred to as SBI-IS-2.

Subsequent regression analysis using those modified SBI-IS-1 and SBI-IS-2 biotic indices showed no relationships with percent deposited sediment at the sample scale.

SBIs – composited reach scale abundances and substrate variables

Analyses of the relationships with the developed biotic indices and stream scale substrate condition variables revealed that only percent embeddedness was significantly related to the developed biotic indices: whole reach SBI-IS (R2 = .88, P = .022), fast water SBI-IS (R2 = .88, P = .014) (Fig. 5-3).

The SBI-CA using reach-scale fast water abundances per stream showed no relationships with any of the proportional sedimentation measures.

Discussion

We developed a sedimentation biotic index using two methods for assigning benthic macroinvertebrate tolerance values, with the two methods relying on taxon frequency/abundance (SBI-IS) of taxa or taxon cumulative abundances (SBI-CA) among percent deposited sediment categories. Using indicator species analysis, we found that the most number of indicator species were associated with a four- level categorization of percent deposited sediment. The highest level of percent deposited sediment within that categorization was shown to be 40 – 100%. That finding, along with the fact that only two of the 30 significant indicator taxa were associated with the 40 – 100% deposited sediment level, suggests that sensitivity to deposited sediments occurs mainly within relatively low levels of percent deposited sediment

(i.e., 0 – 35%). Within that range, 28 taxa were found to be significant indicators among a three level, deposited sediment grouping. Among those taxa, 14 were associated with the 20 - 35% level and 14 taxa were associated with the 10 - 15% deposited sediment level. Taxa within those categories may prove

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effective as indicators of moderate levels of deposited sediment. Likewise, the absence of any of the nine taxa in the lowest percent deposited sediment level may indicate elevated levels of deposited sediments along a stream reach. While tolerance values derived from indicator species analysis were associated with the full range of percent deposited sediment, those derived from the cumulative abundance method resulted in a much lower range of deposited sediment sensitivity. In those habitats, all of the 15 dominant taxa in fast water habitats showed 50% cumulative abundances at ≤ 10% deposited sediment. However, that relatively low level of percent deposited sediment, when compared to indicator taxa associated with the full range of deposited sediment, was probably due to constraining relationships to fast water habitats only.

While the 50% cumulative abundance method focused on fast water relationships only, tolerance values derived from indicator species analysis used reach scale data, irrespective of habitat type. Using indicator species analysis to assign tolerance values using fast water habitats only would have possibly resulted in smaller numbers of indicator species and with those few indicators attributed to a very low deposited sediment range similar to that found using the 50% cumulative abundance method.

Although biotic indices are not intended to be developed using sample-scale data, we chose to analyze our developed SBIs across the full sample dataset due to the ubiquitous distribution and frequency of the dominant taxa across samples and streams. However, the SBI-ISs produced from indicator species analysis relationship with sample-scale percent deposited sediment was not significant. In fact, regression plots consistently showed a slight negative relationship among all sample-scale SBI-ISs and percent deposited sediment. The only significant, sample-scale index was the SBI-CA. That relationship produced a regression plot showing a strong increase in the SBI-CA from 0 – 30% deposited sediment and a downward trend as percent deposited sediment increased, therefore resulting in a low R2 during preliminary analysis. A subsequent analysis using 0 – 30% deposited sediment for the x-axis (i.e., removing 35 –

100% deposited sediment) resulted in a strengthened relationship (R2 = .35). The weak regression relationship found in preliminary analysis that showed a low regression coefficient (R2 = .14) and a downward trendline is probably attributable to the fact that no 50% cumulative abundances occurred over

10% deposited sediment. Zweig and Rabeni (2001) developed the Deposited Sediment Biotic Index

(DSBI) by ranking 50% cumulative abundances of the 30 dominant taxa from glide habitats in warmwater streams. Their developed rankings were associated with a broader range of percent deposited sediment

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levels (0 – 75%), with five taxa showing 50% cumulative abundances from 35 – 75% deposited sediment.

Thus, a dominant taxon associated with a deposited sediment level in that range would contribute to the overall DSBI, whereas we had no taxa occurring above 10% deposited sediment based on 50% cumulative abundance. Including slow water habitats with that method for assigning tolerance values may have provided taxa with 50% cumulative abundances among higher levels of percent deposited sediment.

Analysis of reach scale, SBI-ISs produced from composited taxon abundances showed a robust relationship with percent embeddedness. However modification of the original SBI-IS by removing taxa with moderate tolerance values (i.e., TV = 2 and 3; SBI-IS-1, SBI-IS-2) did not increase the strength of the relationship with reach-scale substrate variables. Furthermore, SBIs produced from slow water taxon abundances showed only slightly weaker relationships with percent embeddedness when compared to SBIs derived from fast water macroinvertebrate abundances (R2 .67 vs. .88, respectively).

Although the SBI-CA showed a moderately strong relationship with percent deposited sediment at the sample scale, it was not related to percent embeddedness. The taxa found using indicator species analysis may have been more related (albeit indirectly) to percent embeddedness because high levels of percent embeddedness may have been more associated with fast water conditions in excess of 10% deposited sediment. Based on our findings, it appears that a sample scale sedimentation biotic index using fast water macroinvertebrate abundance data and tolerance values derived from 50% cumulative abundances should indicate increasing levels of percent deposited sediment in the range from 0 – 30%.

Additionally, the index using tolerance values derived from indicator species analysis and fast water or whole reach macroinvertebrate data should indicate increasing levels of percent embeddedness in pools.

However, further calibration of these indices are warranted to fully understand their potential as effective endpoint measures of sedimentation in the Chattahoochee National Forest.

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Acknowledgements

We thank Craig Roghair and Dan Nuckols of the USFS Center for Aquatic Technology Transfer and Jamie

Roberts for assistance in data collection. We are grateful to Stephen Hiner, Amanda Ellis, Trisha Voshell,

Rachel Wade, and Kathy Hanna for laboratory assistance. Thanks to Charlene Breeden of the USFS

Tallulah Ranger District in Georgia for providing resources in the Chattahoochee National Forest.

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Table 5-1 Number of significant indicator species for seven sedimentation category categorizations.

Percent deposited sediment is shown below the significant number of indicators (shown in bold). Indicator species analysis was conducted for all taxa (n = 64), with taxon abundances combined for each deposited sediment interval within a sedimentation category designation. The third designated group (arrow), with four sedimentation categories, produced the greatest number of significant indicators.

number of significant indicator taxa within a particular percent deposited sediment categorization (1-15) sedimentation category 15 24 30 22 17 25 24 1 0 0-1 0-5 0-5 0-10 0-5 0-10 2 1 5 10-15 10-35 15-25 10-15 15-45 3 5 10 20-35 40-100 30-100 20-100 50-100 4 10 15 40-100 5 15 20-25 6 20 30-45 7 25 50-60 8 30 70-100 9 35 10 40-45 11 50-55 12 60 13 70 14 75-80 15 85-100

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Table 5-2 Indicator taxa of the sedimentation category designation that produced the greatest number of significant indicator values. Sedimentation category corresponds to an increasing level of percent deposited sediment: Group 1 (0-5%), Group 2 (10-15%), Group 3 (20-35%), Group 4 (40-100%).

Functional feeding group (ffg) and habit group are shown for all taxa. Sedimentation category is the tolerance value assigned to each taxa for calculation of the SBI-IS. PR = predator, CG = collector-gatherer,

SC = scraper, SH = shredder, CF = collector-filterer, CR = crawler, BU = burrower, CL = clinger, SP = sprawler.

sedimentation indicator taxa category ffg habit Haloperla sp. 1 PR CR Leptophlebiidae 1 CG CR Habrophlebia vibrans 1 CG CR Cordulegaster sp. 1 PR BU Glossosoma sp., 1 SC CL Tipula sp. 1 SH BU Hexatoma sp. 1 PR CR Tanypodinae sp. 1 PR SP Corbicula sp. 1 CF BU Tallaperla sp. 2 SH CR Perlodidae 2 PR CR Paraleptophlebia sp. 2 CG CR Agapetus sp. 2 SC CL Dicranota sp. 2 PR CR Pteronarcys sp. 3 SH CR Perlidae 3 PR CR Acroneuria sp. 3 PR CR Baetis (complex) 3 CG CL Stenonema sp. 3 SC CL Epeorus sp. 3 CG CL Dolophilodes sp. 3 CF CL Psephenus sp. 3 SC CL Optioservis sp. 3 SC CL Oulimnius sp. 3 SC CL

Anchytarsus sp. 3 SH CL Tanytarsini 3 CF CL Orthocladiinae sp. 3 CG BU Ceratopogonidae 3 PR BU Amphinemura sp. 4 SH CR Diplectrona sp. 4 CF CL

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Table 5-3 Thirty dominant taxa from fast-water habitat macroinvertebrate samples and tolerance values calculated from 50% cumulative abundance across increasing percent deposited sediment. 1 = intolerant

(1% deposited sediment), 2 = moderately tolerant (5% deposited sediment), and 3 = intolerant (10% deposited sediment). Functional feeding group (ffg) and habit are listed for all taxa, abbreviations defined in Table 5.2.

tolerance taxon value ffg habit Ephemerella sp. 1 CG CR Diplectrona modesta 1 CF CL Hexatoma sp. 1 PR CR Tanytarsini 1 CF CL Baetis sp. (complex) 1 CG CL Oligochaeta 1 CG BU Dolophilodes distinctus 1 CF CL Glossosoma sp. 1 SC CL Epeorus sp. 1 CG CL Orthocaldiinae 2 CG BU Oulimnius latiusculus 2 SC CL Optioservus sp. 2 SC CL Stenonema sp. 2 SC CL Perlidae 2 PR CR Rhyacophila sp. 2 PR CR Lanthus sp. 2 PR BU Dicranota sp. 2 PR CR Ceratopogonidae 2 PR BU Prosimulium sp. 2 CF CL Chironomidae 2 CG BU Leuctra sp. 3 SH CR Paraleptophlebia sp. 3 CG CR Tallaperla sp. 3 SH CR Chironominae 3 CG BU Amphinemura sp. 3 SH CR Tanypodinae 3 PR SP Simulium sp. 3 CF CL Tipula sp. 3 SH BU Tipulidae 3 SH BU Remenus sp. 3 PR CR

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Fig. 5-1 Map of the southeastern U.S. showing the location of the Chattooga River watershed and the smaller subwatersheds containing our study sites. See page 92 for stream names and abbreviations.

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Fig 5-2 Regression of sample-scale SBI-CA (n = 127) and percent deposited sediment. Note the range of percent deposited sediment on the x-axis: 0 – 30%.

y = -3.8036x2 + 3.8545x + 1.6603 3.00 R2 = 0.3585

2.50 A 2.00 SBI-C 1.50

1.00 0 0.05 0.1 0.15 0.2 0.25 0.3

percent deposited sediment

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Fig. 5-3 Regressions showing the relationships of percent embeddedness in pools with SBI-IS developed from (A) composited, whole reach, (B) fast-water, and (C) slow-water individual taxa. Each data point represents an index produced from 33 combined macroinvertebrate samples in eight Chattooga River headwater streams.

A. B. 3.3 3.3 R2 = .88 R2 = .86 3.2 3.2

3.1 3.1 3.0 3.0 2.9 SBI-IS SBI-IS 2.9 2.8 2.8 2.7 2.7 2.6 2.5 2.6 0.2 0.3 0.4 0.5 0.6 0.7 0.20.30.40.50.60.7 percent embeddedness percent embeddedness

C. 3.4 R2 = .67

3.2

3.0

SBI-IS 2.8

2.6

2.4 0.2 0.3 0.4 0.5 0.6 0.7 percent embeddedness

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SUMMARY OF CONCLUSIONS

Several studies were conducted to determine relationships of sedimentation, streambeds, and benthic macroinvertebrates in small, forested streams in the Chattahoochee National Forest. The development of sediment load models for the region necessitates a thorough understanding of the influence of streambeds on benthic macroinvertebrate assemblages, with the ultimate goal of establishing numeric biological criteria relating to sedimentation. In working towards that goal, we analyzed existing data and recently acquired data to improve our understanding of streambed substrate influences on benthic macroinvertebrate assemblages. Our first key finding was that macroinvertebrates were weakly related to streambed sedimentation at the reach scale using a random, multihabitat sampling design. In that study, we used methods for deriving sedimentation categories that were developed by the USEPA for determining stream condition status in the Chattooga River watershed. Our results and those of USEPA indicated that distinctive differences did not exist in macroinvertebrate assemblages across the defined sedimentation levels. We report that finding as key because it implies the challenges in establishing and investigating sedimentation impacts in these mountain streams and in determining concurrent physical and biological impacts during 303d stream condition reporting. A second study investigated relationships of macroinvertebrate assemblage traits, which were developed from a stratified multihabitat sampling design, and reach-scale sedimentation. Using that design, several reach scale habitat and substrate variables metrics were found to be significant predictors of six assemblage metrics (Table 2-2). Therefore, those metrics could be used in conjunction with a stratified, multi-habitat sampling design at the reach scale.

Another key finding from that study was that large-scale (multiple kilometer) streambed habitat structure and channel gradient influence macroinvertebrate assemblage structures over a large stream distance

(multiple kilometer), suggesting the need to further investigate impacts among streams defined physically by type (i.e., channel gradient and pool habitat structure). Particular stream types are probably associated with differences in biological impact and differences in sand storage and movement along these headwater stream channels.

In a third study, we summarized macroinvertebrate assemblages over eight headwater streams that contained portions of coarse sand substrate. Our most important findings were that streambeds contained

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few very dominant macroinvertebrate taxa. Of those taxa, most were in the crawler habit group and the collector-gatherer functional feeding group. Additionally, we determined that very few organisms were related to specific types of dominant substrate. Both findings suggest that the crawlers and collector gatherers may be utilizing portions of the streambed containing a variety of substrates including a large portion of coarse sands. Crawlers typically need clean gravel and larger substrate to facilitate feeding and movement. Based on our results, we suggest that the crawler assemblage has adapted to these headwater streambeds by using areas of compacted and clean coarse sand patches. Most importantly, these findings strongly suggest that bedload movement of coarse sands is low and that the sand that does move probably settles out of the water column with certain food content to maintain the high dominance of collector- gatherers and crawlers in these headwater streams. The second key finding in that study was the predominant influence of flow velocity among microhabitat environmental conditions, with deposited sediment significantly correlated to flow conditions but found not to be influencing an assemblage gradient among 264 macroinvertebrate samples. Therefore, flow conditions may primarily influence overall abundances, yet macroinvertebrates appear facultative in substrate micro-habitat requirements among these headwater streambeds, probably due to their ability to maneuver during feeding among a variety of clean substrate types, including portions of compacted sand (within flow velocity constraints).

Separating benthic macroinvertebrate samples and physical conditions allowed us to determine habitat-specific assemblage characteristics and macroinvertebrate response to deposited sediments among fast- and slow-water habitats. From those analyses, we determined that most macroinvertebrates along our streambeds were associated with fast-water habitats, and that macroinvertebrate sensitivity, expressed as correlations of relative abundance per deposited sediment levels, showed that most fast-water taxa prefer low percent deposited sediment levels. As percent deposited sediment increased in that habitat, the relative abundances were greatly reduced. This study also provided a number of metrics related to fast water habitats that can be used as effective metrics for stream monitoring when a random, systematic longitudinal transect design is used for macroinvertebrate sampling (metrics in Table 4-4). However, some of those developed substrate variables are calculated from individual transect observations (e.g., percent transects with sand as dominant substrate), so individual substrate observations per transect are required for developing those measures.

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A final study involved calculating deposited sediment tolerance values using indicator species associations and individual cumulative abundances across percent deposited sediment levels. The final index developed from cumulative abundances showed a relationship with 0 – 30% deposited sediment, and that low range was due to the low deposited sediment levels at which all 50% cumulative abundances fell

(1 - 10%). However, that index may prove effective in determining impacts in streams that may not be physically impacted to a high level (e.g., 40 – 100% deposited sediment conditions), yet are experiencing stress through habitat patches containing ≈30% deposited sediment. The sedimentation index produced from macroinvertebrate indicator species analysis produced a reach-scale index that was related to percent pool embeddedness. This finding bolsters our suggestion that pool habitat structure and conditions may indicate stream channel “type” (Chapter 2), with conditions in those habitats serving as potential surrogate measures of sedimentation for a particular reach and with biological impact manifested and measured within those areas of the streambed that house the most organisms (i.e., fast-water habitats).

The lack of relationships between macroinvertebrates and high levels of deposited sediments found in our studies is consistent with the literature on this topic that has usually indicated subtle biological effects of streambed sedimentation. In the case of our headwater streams, we had no strong, reach-scale gradient of low to high sedimentation and therefore relied on patch-scale influences of sedimentation on benthic macroinvertebrates. The lack of relationships of reach-scale sedimentation with macroinvertebrate assemblages further suggests the difficulty in defining reference streams and streambed conditions, as all streams contain a portion of coarse sands. Therefore, determining overall impacts may require experimental manipulations or investigations of episodic sediment inputs due to environmental disturbances that serve as a proxy for experimental manipulation. However, the former is difficult due to the type of stressor (i.e., deposited sediments) and challenges in treatment application. Additionally, problems arise when determining reference conditions of least-impacted streams because all streams probably maintain at least a moderate level of coarse sand due to widespread historical disturbances and the physical nature of geology and soils in the Southern Blue Ridge region. The eight streams visited in spring

2003 represent typical headwater, mountain streams in the Chattooga River watershed and from those streams we gained insight into the associations of coarse sands and benthic macroinvertebrate assemblages.

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From our overall findings, we generally conclude that headwater streams in the region are resilient to streambed sedimentation consisting of coarse sand. Assemblage resilience is probably due, in part, to (1) good water quality, (2) compaction of streambeds that facilitate movement and food finding, thus high numbers of dominant taxa (3) potential low bedload rates and sand mass movement associated with the quantified flow velocities in our high gradient study streams (maximum flow among 264 samples was 1.2 m/s, with three measurements of 2.0 m/s), (4) the presence of cobble and larger substrate (i.e., macroinvertebrate refugia) that does not become fully embedded due to sedimentation, and (6) the maintanence of riparian vegetation that provide essential allochthonous energy sources to these headwater streams and supports the contemporary, dominant taxa assemblage composition.

Analysis of the five dominant taxa occurring across 264 random, systematic samples in eight, non- adjacent streams in the Chattooga River watershed shows assemblages dominated by collector-gathering

Ephemerella sp. and Paraleptophlebia sp., and Orthocladiinae, and leaf-shredding Leuctra sp. Physical streambed heterogeneity is maintained that includes a moderate level of coarse sands, yet macroinvertebrate dominance indicates a distinct and consistent functional group and habitat group assemblage structure. The presence of those dominant taxa and their ecological characteristics suggests large sand volumes in these streams may not be impacting existing crawlers, shredders, and collector gatherers; all organisms that support important heterotrophy typical of mountain streams. Therefore, streambeds may be maintained functionally through the partitioning of those organisms that consume leaf- matter, collect and gather food resources while crawling along streambeds, and maintain high numbers across streams containing coarse sands. Furthermore, the general assemblage that is utilizing sand patches for crawling and collecting food have adapted compositionally, while showing resilience to 150+ year press disturbance due to historical and contemporary anthropogenic disturbance and natural erosion processes. If the functionally characteristic assemblage structures we found are indeed shaped by rather recent temporal disturbances (e.g., logging and farming in the early 1900s), then their functional adaptability is highlighted.

However, those assemblages are particularly vulnerable due to future changing streambed conditions resulting from watershed disturbances that either change the food base or alter sand volumes and movement in these headwater systems.

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VITA

Scott Longing was born the son of Ernie and Vicki Longing in Russellville, Arkansas on December 13,

1973. He attended London Elementary, Lake Hamilton Junior and Senior High, and Russellville High

School and graduated from the latter in 1992. In 1996, he completed a Bachelor of Science degree in

Biology at Arkansas Tech University. During that year, he was employed as an environmental monitoring technician and conducted air quality monitoring in industrial plants and government buildings during asbestos abatement procedures. He then worked as a laborer foreman during a refueling outage at Arkansas

Nuclear One. In 1998, he was accepted to the Department of Entomology graduate program at the

University of Arkansas. The Masters program in entomology was completed under the direction of Dr. C.

Dayton Steelman in 2001 and involved the distribution of an important beetle disease vector and structural pest associated with poultry operations. Also during that time, he worked with Dr. G.O. Graening and Dr.

Art Brown as part of the Arkansas Subterranean Biodiversity Project and was introduced to cave arthropod taxonomy. Following graduate studies at the University of Arkansas, he worked at the Arkansas State

Plant Board on honeybee, pink bollworm, sweet potato weevil, and red imported fire ant regulations and accounted Arkansas Boll Weevil Eradication assessments. His Ph.D. program in Aquatic Entomology began in 2002 under the direction of Dr. J. Reese Voshell, Jr.

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